In artificial reasoning, constant surmising has become essential for applications that request moment results. Low-idleness models structure the foundation of these high-level frameworks, driving customized suggestions on web-based business sites and empowering constant misrepresentation identification in monetary exchanges.

This blog explores the significance of low-latency models, the challenges in achieving real-time inference, and best practices for building systems that deliver lightning-fast results.

What Are Low-Latency Models?

A low-latency model is an AI or machine learning model optimized to process data and generate predictions with minimal delay. In other words, low-latency models enable real-time inference, where the time between receiving an input and delivering a response is negligible—often measured in milliseconds.

Why Does Low Latency Matter?

• Enhanced User Experience: Instant results improve customer satisfaction, whether getting a movie recommendation on Netflix or a quick ride-hailing service confirmation.
• Basic Navigation: In enterprises like medical care or money, low idleness guarantees opportune activities, such as recognizing expected extortion or distinguishing irregularities in a patient’s vitals.
• Upper hand: Quicker reaction times can separate organizations in a cutthroat market where speed and proficiency matter.

Applications of Low-Latency Models in Real-Time Inference

1. E-Commerce and Personalization

• Constant proposal motors break down client conduct and inclinations to recommend essential items or administrations.
• Model: Amazon’s proposal framework conveys customized item ideas within milliseconds of a client’s connection.
  

2. Autonomous Vehicles

• Autonomous driving systems rely on low-latency models to process sensor data in real-time and make split-second decisions, such as avoiding obstacles or adjusting speed.
• Example: Tesla’s self-driving cars process LiDAR and camera data in milliseconds to ensure passenger safety.
  

3. Financial Fraud Detection

• Low-dormancy models break down continuous exchanges to identify dubious exercises and forestall misrepresentation.
• Model: Installment entryways use models to hail inconsistencies before finishing an exchange.

4. Healthcare and Medical Diagnosis

• In critical care, AI-powered systems provide real-time insights, such as detecting heart rate anomalies or identifying medical conditions from imaging scans.
• Example: AI tools in emergency rooms analyze patient vitals instantly to guide doctors.
  

5. Gaming and Augmented Reality (AR)

• Low-latency models ensure smooth, immersive experiences in multiplayer online games or AR applications by minimizing lag.
• Example: Cloud gaming platforms like NVIDIA GeForce NOW deliver real-time rendering with ultra-low latency.

Challenges in Building Low-Latency Models

Achieving real-time inference is no small feat, as several challenges can hinder low-latency performance:

1. Computational Overheads

• Huge, extraordinary learning models with many boundaries frequently require critical computational power, which can dial back deduction.
  

2. Data Transfer Delays

• Data transmission between systems or to the cloud introduces latency, mainly when operating over low-bandwidth networks.
  

3. Model Complexity

• Astoundingly muddled models could convey definite assumptions to the detriment of all the more sluggish derivation times.
  

4. Scalability Issues

• Handling large volumes of real-time requests can overwhelm systems, leading to increased latency.
  

5. Energy Efficiency

• Low inactivity often requires world-class execution gear, which could consume elemental energy, making energy-useful courses of action troublesome.

Best Practices for Building Low-Latency Models

1. Model Optimization

• Using model tension methodologies like pruning, quantization, and data refining decreases the model size without compromising precision.
• Model: With a redesigned design, Google’s MobileNet is planned for low-inaction applications.

2. Deploy Edge AI

• Convey models nervous gadgets, such as cell phones or IoT gadgets, to eliminate network inactivity caused by sending information to the cloud.
• Model: Apple’s Siri processes many inquiries straightforwardly on gadgets utilizing edge artificial intelligence.

3. Batch Processing

• Instead of handling each request separately, use a small bunching methodology to hold various sales simultaneously, working on overall throughput.
  

4. Leverage GPUs and TPUs

• To speed up deduction times, utilize particular equipment, like GPUs (Illustrations Handling Units) and TPUs (Tensor Handling Units).
• Model: NVIDIA GPUs are generally utilized in computer-based intelligence frameworks for speed handling.
  

5. Optimize Data Pipelines

• Ensure proper data stacking and preprocessing, and change pipelines to restrict delays.
  

6. Use Asynchronous Processing

• Execute nonconcurrent methods where information handling can occur in lined up without trusting that each step will be completed successively.

Tools and Frameworks for Low-Latency Inference

1. TensorFlow Light: TensorFlow Light is intended for versatile and implanted gadgets. Its low inertness empowers on-gadget deduction.

2. ONNX Runtime: An open-source library upgraded for running artificial intelligence models with unrivaled execution and low latency.

3. NVIDIA Triton Induction Server is a versatile solution for conveying computer-based intelligence models with constant monitoring across GPUs and central processors.

4. PyTorch TorchScript: Permits PyTorch models to run underway conditions with enhanced execution speed.

5. Edge AI Platforms: Frameworks like OpenVINO (Intel) and AWS Greengrass make deploying low-latency models at the edge easier.

Real-Time Case Studies of Low-Latency Models in Action

1. Amazon: Real-Time Product Recommendations

Amazon’s suggestion framework is an excellent representation of a low-inertness model. The organization utilizes ongoing derivation to investigate a client’s perusing history, search inquiries, and buy examples and conveys customized item proposals within milliseconds.

How It Works:

• Amazon’s simulated intelligence models are streamlined for low inactivity utilizing dispersed registering and information streaming apparatuses like Apache Kafka.
• The models use lightweight calculations that focus on speed without compromising exactness.

Outcome:

• Expanded deals: Item suggestions represent 35% of Amazon’s income.
• Improved client experience: Clients get applicable suggestions that help commitment.

2. Tesla: Autonomous Vehicle Decision-Making

Tesla’s self-driving vehicles depend vigorously on low-idleness artificial intelligence models to go with constant choices. These models interact with information from numerous sensors, including cameras, radar, and LiDAR, to recognize snags, explore streets, and guarantee traveler security.

How It Works:

• Tesla uses edge computerized reasoning, where low-lethargy models are conveyed clearly on the vehicle’s introduced hardware.
• The system uses overhauled cerebrum associations to recognize objects, see directions, and control speed within a fraction of a second.

Outcome:

• Real-time decision-making ensures safe navigation in complex driving scenarios.
• Tesla’s AI system continues to improve through fleet learning, where data from all vehicles contributes to better model performance.

3. PayPal: Real-Time Fraud Detection

PayPal uses low-latency models to analyze millions of transactions daily and detect fraudulent activities in real-time.

How It Works:

• The organization utilizes AI models enhanced for rapid derivation fueled by GPUs and high-level information pipelines.
• The model’s screen exchange examples, geolocation, and client conduct immediately hail dubious exercises.

Outcome:

• Reduced fraud losses: PayPal saves millions annually by preventing fraudulent transactions before they are completed.
• Improved customer trust: Users feel safer knowing their transactions are monitored in real-time.

4. Netflix: Real-Time Content Recommendations

Netflix’s proposal motor conveys customized films and shows ideas to its 230+ million supporters worldwide. The stage’s low-idleness models guarantee suggestions are refreshed when clients connect with the application.

How It Works:

• Netflix uses a hybrid of collaborative filtering and deep learning models.
• The models are deployed on edge servers globally to minimize latency and provide real-time suggestions.
  

Outcome:

• Expanded watcher maintenance: Continuous proposals keep clients drawn in, and 75% of the content watched comes from simulated intelligence-driven ideas.
• Upgraded versatility: The framework handles billions of solicitations easily with insignificant postponements.

5. Uber: Real-Time Ride Matching

Uber’s ride-matching estimation is the incredible delineation of genuine low-torpidity artificial brainpower. The stage processes steady driver availability, voyager requests, and traffic data to organize riders and drivers beneficially.

How It Works:

• Uber’s artificial intelligence framework utilizes a low-dormancy profound learning model enhanced for constant navigation.
• The framework consolidates geospatial information, assesses the season of appearance (estimated arrival time), and requests determining its expectations.

Outcome:

• Reduced wait times: Riders are matched with drivers within seconds of placing a request.
• Upgraded courses: Drivers are directed to the speediest and most proficient courses, working on and by with enormous productivity.

6. InstaDeep: Real-Time Supply Chain Optimization

InstaDeep, a pioneer in dynamic simulated intelligence, uses low-idleness models to improve business store network tasks, such as assembly and planned operations.

How It Works:

• InstaDeep’s artificial intelligence stage processes enormous constant datasets, including distribution center stock, shipment information, and conveyance courses.
• The models can change progressively to unanticipated conditions, like deferrals or stock deficiencies.

Outcome:

• Further developed proficiency: Clients report a 20% decrease in conveyance times and functional expenses.
• Expanded flexibility: Continuous advancement empowers organizations to answer disturbances right away.

Key Takeaways from These Case Studies

1. Continuous Pertinence: Low-inactivity models guarantee organizations can convey moment esteem, whether extortion anticipation, customized proposals, or production network enhancement.
2. Versatility: Organizations like Netflix and Uber demonstrate how low-dormancy artificial intelligence can manage monstrous client bases with negligible deferrals.
3. Innovative Edge: Utilizing edge processing, improved calculations, and disseminated models is urgent for continuous execution.

Future Trends in Low-Latency Models

1. Combined Learning: Appropriate simulated intelligence models permit gadgets to learn cooperatively while keeping information locally, lessening dormancy and further developing security.

2. High-level Equipment: Developing artificial intelligence equipment, such as neuromorphic chips and quantum registering, guarantees quicker and more proficient handling for low-inertness applications.

3. Mechanized Improvement Devices: simulated intelligence apparatuses like Google’s AutoML will keep working on models’ streamlining for continuous derivation.

4. Energy-Effective artificial intelligence: Advances in energy-proficient computer-based intelligence will make low-idleness frameworks more maintainable, particularly for edge arrangements.

Conclusion

As computer-based intelligence reforms businesses, interest in low-dormancy models capable of constant surveillance will develop. These models are fundamental for applications where immediate arrangements are essential, such as independent vehicles, extortion discovery, and customized client encounters.

Embracing best practices like model enhancement and edge processing and utilizing particular devices can assist associations in building frameworks that convey lightning-quick outcomes while maintaining accuracy and adaptability. The fate of simulated intelligence lies in its capacity to act quickly, and low-dormancy models are at the core of this change.

Begin constructing low-idleness models today to ensure your computer-based intelligence applications remain competitive in a world that demands speed and accuracy.

How can [x]cube LABS Help?

[x]cube LABS’s teams of product owners and experts have worked with global brands such as Panini, Mann+Hummel, tradeMONSTER, and others to deliver over 950 successful digital products, resulting in the creation of new digital revenue lines and entirely new businesses. With over 30 global product design and development awards, [x]cube LABS has established itself among global enterprises’ top digital transformation partners.

Why work with [x]cube LABS?

• Founder-led engineering teams:

Our co-founders and tech architects are deeply involved in projects and are unafraid to get their hands dirty. 

• Deep technical leadership:

Our tech leaders have spent decades solving complex technical problems. Having them on your project is like instantly plugging into thousands of person-hours of real-life experience.

• Stringent induction and training:

We are obsessed with crafting top-quality products. We hire only the best hands-on talent. We train them like Navy Seals to meet our standards of software craftsmanship.

• Next-gen processes and tools:

Eye on the puck. We constantly research and stay up-to-speed with the best technology has to offer. 

• DevOps excellence:

Our CI/CD tools ensure strict quality checks to ensure the code in your project is top-notch.

Contact us to discuss your digital innovation plans. Our experts would be happy to schedule a free consultation.

The post Real-Time Inference and Low-Latency Models appeared first on [x]cube LABS.

Organizations are bombarded with information from various sources in today’s data-driven world. Data is an invaluable asset, but it can quickly become a burden without proper organization and management.

What is data architecture?

Data architecture is the blueprint for how your organization manages its data. It defines the structure, organization, storage, access, and data flow throughout its lifecycle. Think of it as the foundation upon which your data ecosystem is built.

Why is Data Architecture Important?

A well-defined data architecture offers a multitude of benefits for organizations. Here’s a glimpse of the impact it can have:

• Improved Decision-Making: By ensuring data accuracy and consistency across the organization, data architecture empowers businesses to make data-driven decisions with confidence. A study by Experian revealed that companies with a well-defined data governance strategy are 2.6 times more likely to be very satisfied with their overall data quality.
  
• Enhanced Efficiency: A structured data architecture eliminates data silos and streamlines data access. This results in increased operational effectiveness and decreased time spent searching for or integrating data from disparate sources.
  
• Boosted Compliance: Big data architecture is crucial in data governance and compliance. By establishing clear data ownership and access controls, businesses can ensure they adhere to legal regulations and mitigate data security risks.
  
• Scalability for Growth: A well-designed data architecture is built with flexibility in mind. As a result, businesses can expand their data infrastructure seamlessly and accommodate future data volume and complexity growth.
  

The Challenges of Unstructured Data

Without a data architecture, organizations face a multitude of challenges:

• Data Silos: Data gets fragmented and stored in isolated locations, making it difficult to access and analyze.
  
• Data Inconsistency: Consistent data definitions and formats lead to errors and poor data quality.
  
• Security Risks: Uncontrolled data access and lack of proper security measures increase the risk of data breaches.
  
• Slow Decision-Making: The time and effort required to locate and integrate data significantly slow the decision-making process.

Critical Components of a Data Architecture

A robust data architecture relies on core elements working together seamlessly, like a well-built house requiring a solid foundation and essential components. Here’s a breakdown of these critical components:

• Data Governance is the general structure used to manage data as a strategic asset. It establishes roles, responsibilities, and processes for data ownership, access control, security, and quality. A study by Gartner revealed that 80% of organizations plan to invest in data governance initiatives in the next two years, highlighting its growing importance.
  
• Data Modeling: This involves defining the structure and organization of data within your data storage systems. Data models ensure consistency and accuracy by establishing clear definitions for data elements, their relationships, and the rules governing their use.
  
• Data Storage: Choosing the proper data storage solutions is crucial. Common options include:
  
  • Relational databases: Structured data storage ideal for transactional processing and queries (e.g., customer information, product catalogs).
    
  • Data warehouses: Designed for historical data analysis, Data warehouses combine information from multiple sources into one central location for in-depth reporting. According to a study by Invetio, 63% of businesses leverage data warehouses for advanced analytics.
    
  • Data lake architecture provides a scalable and adaptable method for storing substantial amounts of information and semi-structured and unstructured data.
    
• Data Integration: Organizations often have data scattered across different systems. Data integration strategies combine data from various sources (databases, applications, external feeds) to create a unified view for analysis and reporting.
  
• Data Security: Protecting private information against illegal access, alteration, or loss is paramount. Data security measures include encryption, access controls, and intrusion detection systems.
  
  The IBM Cost of a Data Breach Report 2023 indicates that the global average data breach expense attained a record high of $4.35 million, highlighting the financial impact of data security breaches.
  
• Data Quality: Ensuring data accuracy, completeness, consistency, and timeliness is essential for reliable analysis and decision-making. Data quality management processes involve cleansing, validation, and monitoring to maintain data integrity. Poor data quality costs US businesses an estimated $3.1 trillion annually, according to a study by Experian.
  
• Metadata Management: Metadata provides vital information about your data – its definition, lineage, usage, and location. Effective metadata management facilitates data discovery, understanding, and governance.

The Data Architecture Design Process

Building a data architecture isn’t a one-size-fits-all approach. The design process should be tailored to your organization’s needs and goals. Here’s a roadmap to guide you through the essential steps:

1. Define Business Goals and Data Requirements: Understanding your business objectives is the foundation of a successful data architecture. It is crucial to identify KPIs (key performance indicators) and the information needed to monitor them.
   
   For example, an e-commerce platform might focus on KPIs like customer acquisition cost and conversion rate, requiring data on marketing campaigns, customer demographics, and purchasing behavior.
   
2. Analyze Existing Data Landscape: Before building new structures, it’s essential to understand your current data environment. This involves taking stock of existing data sources (databases, applications, spreadsheets), data formats, and data quality issues.
   
   A study by Informatica found that only 12% of businesses believe their data is entirely accurate and usable, highlighting the importance of assessing your current data landscape.
   
3. Select Appropriate Data Management Tools and Technologies: You can select the right tools and technologies by clearly understanding your data needs. This includes choosing data storage solutions (relational databases, data warehouses, data lakes), data integration tools, and data governance platforms.
   
4. Develop an Implementation Plan with Clear Phases and Milestones: A well-defined implementation plan breaks down the data architecture project into manageable phases. Each phase should have clear goals, milestones, and resource allocation. This keeps the project on course and delivers value incrementally.
   

Additional Considerations:

• Scalability: Design your data architecture with future growth in mind. Choose technologies and approaches that can accommodate increasing data volumes and user demands.
  
• Security: Data security should be a top priority throughout the design process. Strong security measures should be put in place to safeguard private data.
  
• Data Governance: Clearly define the rules and processes to ensure compliance with data ownership, access control, and regulation.

Building and Maintaining Your Data Architecture

Having a well-defined data architecture design is just the first step. Now comes the crucial task of implementing and maintaining your data infrastructure. Here’s a breakdown of critical practices to ensure a smooth transition and ongoing success:

Implementing Your Data Architecture:

• Data Migration and Transformation: Moving data from existing systems to your new architecture requires careful planning and execution. Best practices include:
  
  • Data cleansing: Identify and address data quality issues before migration to ensure data integrity in the new system.
    
  • Data transformation: Transform data into the format and structure your target data storage solutions require. According to a study by CrowdFlower, 80% of data science projects experience delays due to data quality and integration issues.
    
• Setting Up Data Pipelines: Data pipeline architecture automates the movement and integration of data between various sources and destinations. This ensures data is continuously flowing through your data architecture, enabling real-time insights and analytics.
  

Maintaining Your Data Architecture:

• Data Monitoring: Continuously monitor the health and performance of your data architecture. This includes tracking data quality metrics, identifying potential bottlenecks, and ensuring data pipelines function correctly.
  
• Data Auditing: Establish data auditing processes to track data access, usage, and changes made to the data. This helps maintain data integrity and regulatory compliance.
  

Additional Considerations:

• Data Governance in Action: Enforce data governance policies and procedures throughout the data lifecycle. This includes training users on data access protocols and ensuring adherence to data security measures.
  
• Change Management: Be prepared to adapt your data architecture as your business evolves and data needs change. Review your data architecture regularly and update it as necessary to maintain alignment with your business goals.
  

The Importance of Ongoing Maintenance:

Maintaining your data architecture is an ongoing process. By continuously monitoring, auditing, and adapting your data infrastructure, you can ensure it remains efficient, secure, and aligns with your evolving business needs.

This ongoing effort is vital for maximizing the return on investment in your data architecture and unlocking the true potential of your data assets.

Benefits of a Well-Designed Data Architecture

• Improved data quality and consistency
• Enhanced decision-making capabilities
• Increased operational efficiency
• Streamlined data governance and compliance
• Scalability to accommodate future growth

Case Studies: Successful Data Architecture Implementations

Data architecture isn’t just a theoretical concept; it’s a powerful tool companies leverage to achieve significant business results. Here are a few inspiring examples:

• Retail Giant Optimizes Inventory Management: A major retail chain struggled with stockouts and overstocking due to siloed data and inaccurate inventory levels. By implementing a unified data architecture with a central data warehouse architecture, they gained real-time visibility into inventory across all stores.
  
  This enabled them to optimize stock levels, reduce lost sales from stockouts, and improve overall inventory management efficiency. Within a year of implementing the new data architecture, the company reported a 15% reduction in out-of-stock rates.
  
• Financial Institution Reaps Benefits from Enhanced Fraud Detection: Like many in the industry, financial institutions face challenges in detecting fraudulent transactions due to fragmented customer data and limited analytics capabilities.
   
  However, by implementing a data architecture that integrated customer data from various sources and enabled advanced analytics, they could more effectively identify suspicious patterns and activities. This led to a 20% decrease in fraudulent transactions, significantly improving their security measures.
  
• Healthcare Provider Improves Patient Care: A healthcare provider aims to improve patient care coordination and treatment effectiveness. They implemented a data architecture that integrated lab results, patient information from electronic health records, and imaging studies.
  
  This gave doctors a holistic view of each patient’s medical background, empowering them to make better-educated treatment decisions and improve patient outcomes. The healthcare provider reported a 10% reduction in hospital readmission rates after implementing the new data architecture.

These are just a few examples of how companies across various industries have leveraged data architecture to achieve their business goals. By implementing a well-designed and well-maintained data architecture, organizations can unlock the power of their data to:

• Boost operational efficiency
• Enhance decision-making capabilities
• Gain a competitive edge
• Deliver exceptional customer experiences
  

Conclusion

Implementing a robust data architecture is essential for businesses looking to maximize the possibilities of their data assets. By incorporating key components such as data governance, data modeling, data storage, data integration, data security, data quality, and metadata management, companies can ensure their data is accurate, secure, and readily accessible for informed decision-making. 

A well-structured data architecture provides a strategic framework that supports the efficient management of data and enhances its value by facilitating seamless integration and utilization across the enterprise.

As data grows in volume and complexity, investing in a comprehensive data architecture becomes increasingly critical for achieving competitive advantage and driving business success. 

By following industry standards and continuously improving their data architecture, organizations can stay ahead in the ever-evolving landscape of data management, ensuring they remain agile, scalable, and capable of meeting their strategic goals.

How can [x]cube LABS Help?

[x]cube LABS’s teams of product owners and experts have worked with global brands such as Panini, Mann+Hummel, tradeMONSTER, and others to deliver over 950 successful digital products, resulting in the creation of new digital revenue lines and entirely new businesses. With over 30 global product design and development awards, [x]cube LABS has established itself among global enterprises’ top digital transformation partners.

Why work with [x]cube LABS?

• Founder-led engineering teams:

Our co-founders and tech architects are deeply involved in projects and are unafraid to get their hands dirty. 

• Deep technical leadership:

Our tech leaders have spent decades solving complex technical problems. Having them on your project is like instantly plugging into thousands of person-hours of real-life experience.

• Stringent induction and training:

We are obsessed with crafting top-quality products. We hire only the best hands-on talent. We train them like Navy Seals to meet our standards of software craftsmanship.

• Next-gen processes and tools:

Eye on the puck. We constantly research and stay up-to-speed with the best technology has to offer. 

• DevOps excellence:

Our CI/CD tools ensure strict quality checks to ensure the code in your project is top-notch.

Contact us to discuss your digital innovation plans, and our experts would be happy to schedule a free consultation.

The post Designing and Implementing a Data Architecture appeared first on [x]cube LABS.

Introduction

Creating an efficient database schema is critical for any organization that relies on data to run its operations. A well-designed schema can help with data management, system performance, and maintenance costs. A crucial step in product engineering is designing an effective database schema, which calls for careful consideration of several aspects, including scalability, performance, data integrity, and simplicity of maintenance.

This article will give us fundamental principles and best practices to remember when creating an efficient database schema.

Identify the data entities and relationships.

Identifying them and their relationships is the first step in designing an efficient database schema. This can be accomplished by analyzing business requirements and identifying key objects and concepts that must be stored in the database.

Once the entities have been identified, their relationships must be defined, such as one-to-one, one-to-many, or many-to-many.

Normalize the data

Normalization is the process of combining data in a database to reduce redundancy and improve data integrity. There are several levels of normalization, with the first, second, and third standard forms being the most commonly used. Normalization prevents data duplication and ensures that updates are applied consistently throughout the database.

Use appropriate data types: Selecting the correct data type for each column is critical to ensure the database is efficient and scalable. For example, using an integer data type for a primary key is more efficient than using a character data type.

Similarly, using a date data type for date columns ensures fast and accurate sorting and filtering operations.

Optimize indexing

Indexing improves query performance by creating indexes on frequently used columns in queries. Based on the column’s usage pattern, the appropriate type of index, such as clustered or non-clustered, must be selected. On the other hand, over-indexing can cause the database to slow down, so it’s essential to strike a balance between indexing and performance.

Consider partitioning

Partitioning is a technique for dividing a large table into smaller, more manageable sections. This can improve query performance, speed up backup and restore operations, and make maintenance easier. Date ranges, geographic regions, and other logical groupings can all be used to partition data.

Use constraints and triggers.

Rules and triggers can improve data integrity and consistency. For example, a foreign key constraint can help prevent orphaned records in a child table, whereas a check constraint can ensure that only valid data is entered into a column. Triggers can also be used to impose business rules and validate complex data.

Plan for future scalability

Creating an efficient database schema entails optimizing performance today and planning for future scalability. This entails scheduling for future growth and designing the system to accommodate it. Partitioning large tables, optimizing indexes, and preparing for horizontal scaling with sharding or replication can all be part of this.

Conclusion

Finally, designing an efficient database schema necessitates careful planning and considering numerous factors. By following the best practices outlined in this article, you can create an efficient, scalable, and maintainable schema that meets your organization’s product engineering needs now and in the future.

Read more.

The post How to Design an Efficient Database Schema? appeared first on [x]cube LABS.

The debate on how “private” our online activities are is probably going to take center stage in 2021 and beyond. With increasing exposure to emerging technology, dependence on various ecosystems which keep our contacts, messages, search histories and more in sync, this topic has been gaining prominence for a while, but at the end of last year, the very public tussle between two of the biggest corporations in the world, namely Apple and Facebook, has kicked things into high gear. With both companies trying to justify their actions with elaborate marketing messages and in-your-face ads, the consumers have begun to educate themselves further on how deeply companies pry into personal lives and how bad could things become in the event of a data breach.

Adding to the mix is the 2020 docu-drama “The Social Dilemma” which aptly summarized the prevailing mantra of online platforms as “if you’re not paying for the product, then you are the product”. Over the years, platforms such as Facebook, WhatsApp, Twitter and ecosystems built by Apple and Google have become such integral parts of our lives that these companies probably know more about our lives than they have any right to. As more companies look to upsell and cross-sell new products and services through the first product that we pick up from them, we will be unwittingly giving away more of our information by allowing companies to track a variety of online activities that we indulge in. Using that information, companies will then try to sell us more of their products or those of their affiliates.

End of last year saw a war break out between a company which presumably has the highest regard for user privacy and another which again, presumably, has the least concern for the same. Apple, the former, revealed that with an upcoming update to their iOS 14, they will ask companies which track user activity through the web, ask for permission before doing so. Facebook, the latter, was incensed and came out all guns blazing against a move, which in their opinion, would crush small businesses worldwide that depend upon targeted ads to reach specific groups of users who’d be interested in buying from them.

The latest row has been brewing for a while though, almost a decade, if we do some digging. Apple’s philosophy has always been that the internet is an extension of the personal computing space, with the smartphone being the most personal device of all. As for Facebook, it has increasingly ventured into enabling consumers to launch digital commerce ventures by taking advantage of the vast amount of data it has at its disposal. The recent launch of Facebook marketplace has further established this intent. The cost of using their free service, as per Facebook, is access to browsing data which can then be leveraged for ads. Till date, most of Facebook’s millions of users had no clue exactly what they are allowing the company to do, and with controls to disable tracking buried deep into privacy settings, monitoring was difficult for everyone except the informed few. However, with Apple making it simple by popping up a single message which would turn tracking completely off, it was time for Facebook to be worried.

The battle has now amped up to the extent that it might be brought to court very soon. Both companies held press conferences this week to talk about their respective quarterly performances and apart from the financials, both Tim Cook and Mark Zuckerberg mentioned the burning issue at hand. The Facebook CEO was dismissive of Apple’s criticism saying it’s not coming from a virtuous angle, but from the perspective of protecting Apple’s interests. After targeting Apple with full-page newspaper ads in December, Facebook is now reportedly preparing a lawsuit that will accuse Apple of using its market position to damage parties like Facebook and others who are presented as the saviours of small businesses.

For Apple, this isn’t new. For a company that just had its best quarter ever even in the middle of a pandemic, the command it has over the market and the influence that power can exert is undeniable. Just a few months back, we witnessed the Apple vs Epic Games dispute where the latter tried to bypass Apple’s in-app purchase mechanism in its game, Fortnite. The result wasn’t very favorable for Epic in the end and if prevailing opinion is anything to go by, Facebook doesn’t find itself in a favorable position either.

Tim Cook, in his disclosure yesterday, made it very clear that Facebook’s protests aren’t going to sway Apple’s opinions at all and the tech giant will be very much moving forward with the update to its operating systems which will make users aware of data tracking by apps and ask for their permission.

“At a moment of rampant disinformation and conspiracy theories juiced by algorithms,” Cook said, “we can no longer turn a blind eye to a theory of technology that says all engagement is good engagement — the longer the better — and all with the goal of collecting as much data as possible.” Apple also released an infographic detailing how data is tracked and used by various companies and what’s their take on it. Clearly taking the context of the ongoing dialogue surrounding data privacy to present their case.

Sensing the increasing awareness among the general public about this issue, Facebook knows their stance won’t be that popular. The company has already expressed apprehension about less number of users giving them access to data and the resultant obstacles it will have to face in the business of targeted ads. However, they will go ahead with the lawsuit to keep the growing number of businesses using their platform happy and leverage their own considerable influence to work out a few favorable terms if not achieve complete victory. Helping their cause would be the fact that in the wake of COVID-19, more retailers would bring their businesses online and depend on Facebook’s huge user base to reach potential customers. Facebook would definitely argue that it is taking a leading role in helping people affected by the pandemic to turn things around and it’s insensitive of Apple to throw a spanner in those efforts with their obsession with privacy.

This battle will be in the works for a while and we will stay tuned to see how it plays out. If Facebook decides to indeed sue Apple and attempt to prevent the iOS update from hitting consumer devices, we could be looking at a lengthy battle in the days to come. While digital adoption is the way to go for businesses to become more efficient, cost-effective and customer friendly, the world is waking up to how much of their personal information big tech companies actually have and the sway it gives them as a result. Discussions are raging on the possibilities of companies holding national governments ransom, influencing decisions and increasingly commodifying their customers. While walking the thin line between keeping business interests intact and avoiding alienating users, companies have to be extremely wary of missteps and the winner of this battle between Apple and Facebook will give them an idea about which side of the line they need to lean towards in formulating policies for the new decade.

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Need for efficiency in agriculture- more than ever!

In one of our earlier blog post, Agriculture to Agritech: Trends, Challenges, and the Path Forward with Digital Technology and Software Solutions,  we projected that the agriculture industry would feed an estimated global population of 9.7 billion by 2050. In 2020 alone, a 60% increase is required to feed the population. We talked about macroeconomics, changing consumer preferences, emerging technologies and transforming supply chains as the key drivers for digital transformation in agriculture and how the challenges facing the agriculture industry worldwide could be effectively tackled by following the right approach and leveraging technology to meet the growing demand for food.

Until now, factors such as climate change, population growth and food security concerns have propelled the industry into seeking more innovative approaches to improve crop yield. But the current COVID-19 crisis has further exposed the vulnerability of the agricultural landscape and raised questions about meeting the global food demands sustainably, with adverse factors at play. Again, the answer lies in achieving efficiency- producing more with less, now more than ever.

Data and AI- The answer to meeting challenges efficiently

A farmer in Texas observed the direction of the wind and estimated that a swarm of grasshoppers was likely to settle down near the southwest corner of his farm. But before he could direct his pesticides in the southwest corner, he got an alert on his smartphone from the AI and data company he hired to monitor his farm which showed new satellite images against pictures of the same box over a five-year period. Their AI algorithm detected that the insects had landed in a different corner of the field. The warning turned out to be accurate and enabled the farmer to remove pests from his field in time.

Another interesting use of AI is how NatureFresh Farms grow greenhouse tomatoes in a bed of pulped coconut husks. Their environment allows growers to completely control which nutrients go into the plant. Their sensors monitor the fruit’s progress as they turn ripe and adjust light to accelerate or slow the pace of growth.

Today, agriculture stakeholders are processing data to reduce the impact of adverse situations and achieve an optimal output. The majority of agriculture startups are switching to AI-enabled solutions to increase the efficiency of agricultural production. Implementing these solutions could not only help in increasing the production with limited resources but also detect diseases or climate changes sooner and enable growers to respond timely.

The market prospects are in favor, too.

AI in agriculture market was valued at around USD 545 million in 2017 and is expected to reach approximately USD 2075 million by 2024, at a CAGR of 21% between 2018 and 2024. This growth is attributed to the fact that AI proposes direct solution to some of the key challenges that hindered the digital growth in agriculture.

1. Agritech industry has huge untapped potential. However, it’s advancement relies heavily on data. The agriculture market produces data at intermittent intervals annually, thereby making data collection slow and challenging. AI-powered devices have enabled quick and reliable data collection and made agritech a promising market.
2. It is estimated that by 2050, 66% of the global population will reside in urban areas. This means that the workforce in rural areas will be greatly reduced. It has, therefore, become imperative to develop cognitive systems using AI and other innovative technologies that can ease up farmers’ work significantly and compensate for the reduced number of people working on the farm.
3. The uncertainty in agriculture industry can be addressed by using AI solutions that can effectively identify potential risks and resolve issues before they occur. Timely warnings and insights will enable farmers to make more rapid and informed decisions, thereby reducing the surprise of crop loss at the time of harvest.

Addressing the needs- How an agricultural stakeholder can benefit from Data and AI.

As the current adversity in agriculture drives the need for innovation across the entire ecosystem, stakeholders continue to explore use cases of Data and AI, making it emerge as the much needed technological innovation to address the current and future needs.

1. Intelligent Remote monitoringSuccessfully producing the desired yield in terms of quantity as well as quality has become a game of numbers, and these numbers are what sensors and IoT devices provide us. Farmers can leverage information gained from AI-driven sensors to make changes in processes by adjusting inputs to improve operations and efficiencies. This data can also be shared globally within the community to maintain a central knowledge repository that enables better decisions.The scope of using these sensors is wide. These sensors can be ground, aerial, or machine-based. Each one of them holds huge potential for agricultural production. The data collected from on-ground sensors can help decide the best place to plant for highest yield, the amount that should be planted to avoid waste, etc. Drones and satellites can monitor crop health and pest disease and help prevent crop loss. Farm equipment can also capture data on expected crop production. Automated planting equipment can provide farmers with estimates on crop yield and harvest output, allowing them to plan for sales forecasting, overflow and shortage.
2. Tackling labor challengesWith fewer people entering the farming profession, most farms were already facing the challenge of a workforce shortage. The current lockdown caused by COVID-19 has further highlighted the need to find an alternative solution to manage farms in the absence of manual labor. One solution to help with this shortage of farmers is AI agriculture bots. These bots increase the human labor workforce capacity and are used in various forms- they can harvest crops at a higher volume and faster pace than human laborers, identify weeds more accurately and eliminate them, reduce costs for farms by having a round the clock labor force and much more.
3. Optimizing outputAI can help farmers choose the right type of crop. Based on data, they can determine the right mix of crops that are customised for various needs and weather. AI technologies can also provide insights on how a particular type of seed will react to a particular type of soil profile, local climate conditions and weather forecasts. By correlating and analyzing all this information, the year-to-year outcome can be optimized and consequently, ROI can be maximized.
4. Research and DevelopmentAI is helping speed up trials in agriculture by decreasing the length of the trial and error phase of development. Research has found that algorithms can help determine which hybrid plants would grow best in certain real-life environmental conditions and this can save a massive amount of time and effort. Discovery to commercialization of hybrid plants could take years. Algorithms can save considerable amount of time in the process. For instance, a corn hybrids breeding program that selected 500 breeds for trails- completely cost and time prohibitive, used an algorithm based on fifteen years of molecular marker and field trail information and saved almost one year out of estimated eight, which is an incredible leap, considering the population surge.
5. Virtual assistanceAlthough chatbots are prevalent mostly in retail, media, travel and insurance companies, they have great potential in agriculture as well. Virtual assistants in the form of chatbots, powered by AI and integrated with machine learning, have been developed specifically for farmers. The chatbots also support natural language processing, for farmers’ ease of use. Farmers can not only gain the needed assistance and get their queries answered, but also receive recommendations and advice on particular farm issues.
6. Image RecognitionImage recognition is another advancement that would allow farmers to monitor their land and crops more quickly and efficiently, and also understand past patterns over time. AI is being trained to recognize over 5000 species of plants and animals, which would improve drone ability to identify pest disease and crop damage. Unwanted plants growing in farms can also be detected by combining image processing and machine learning techniques.  Image processing can also be used in fruit grading systems to segment and classify with great accuracy. With correct imaging techniques and algorithms, the classification accuracy of up to 96% can be obtained.
7. Synergy of AI and IoTAI enables IoT to achieve its full potential. The data collected from multiple sources is evaluated through machine learning abilities. Using AI systems, analysts can find correlations among large volumes of data coming from a  multiplicity of sources, such as historic data on weather, surveys, market news, soil information and images, and much more. These systems can help extract useful insights and the concerned stakeholders can benefit from the recommendations.

Conclusion

AI integration in agriculture has been relatively late, but the plethora of opportunities to explore and to lead agriculture industry towards greater sustainability is vast. As we discussed, AI can quickly recognize likely threats and recommend specific actions that are required to overcome them. It can be used in agriculture to enhance outcomes with minimum environmental damage. Not just that, AI can identify a crop disease with 98% accuracy or adjust greenhouse conditions to accelerate or decelerate crop growth. It is certain that efficient use of AI has the potential to actually meet the food requirements of the world but what remains to be seen is how the stakeholders in the agriculture industry- agribusinesses, farmers, investors and other players harness its potential.

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Most enterprises, we’ve observed, find launching digital initiatives and undertaking digital transformation in general to be a relatively easy and low-risk undertaking, at least from a cost perspective. Where enterprises typically spend in 7 digits for legacy technology initiatives, digital initiatives typically only require a 6 digit spend. At least from a budgeting and expenses perspective enterprises rarely hit a major roadblock when it comes to digital transformation.

If it’s not spend that holds enterprises back in delivering successful digital initiatives, then what other factors are at play? After all, time is running out! As detailed and analysed in this Forbes article, the majority of enterprises admit they have only a couple of years to transform or risk falling behind.

There are many reasons, of course, including experience and skills of the teams undertaking these initiatives, the organizational culture and the pace of adaptation to digital, and more. Another critical factor, and the one that’s the focus of this writeup, is: data. Where digital transformation initiatives falter, typically, pertains to the specifics: what precise initiative is planned, how the details are worked out, how the idea is validated, and more. Just as importantly, innovation efforts rarely see success immediately on launch, and need a patient and iterative approach before clear success can be seen.

Here’s Where Data Comes into Play

Enterprises need to leverage data carefully at the planning stage as well as at the execution and support stage. At the planning level, stage, data needs to guide what assumptions are made, what level of customer understanding drives the initiatives, and so on. And once the initiatives make contact with the real world, and are in the hands of real customers, enterprises need to take a patient, iterative approach that carefully leverages data to ensure that they see success over time.

While leveraging data effectively sounds simple enough in theory, it is actually far harder to manage in practice.

Case in Point

A prominent brick-and-mortar services organization that we worked with, was eager to undertake digital transformation, but they seemed to approach it as a checklist of initiatives, rather than as a business transformation effort where each technology initiative is tied into clear business metrics and CX transformation goals. When we recommended a comprehensive mobile strategy for them, their response was that they already had a mobile app. When we chose to dig deep into the mobile app however, we quickly discovered problems. The app lacked a certain amount of empathy, and failed to prioritise the way customers would want to use the app and the workflow they would prefer, and seemed to have too many friction points that customers found frustrating.

How could this have been avoided? The key of course, is to achieve a shift in perspective: enterprises need to switch from prioritising their own problems and seeking their own benefits, to prioritizing customer needs and customer perspective. This is of course very hard to do, which is why leveraging available data to deliver critical insights that help you plan your overall digital strategy as well as execute specific digital initiatives is critical.

That’s just what we did with this organization, and tossed in key terms like ‘Customer Experience’ and ‘Customer Centricity’ for good measure. Eventually, with the data we gathered from actual customers who provided extensive feedback, along with the insights we gleaned from the usage of their currently faltering mobile channel, the organisation came around and we worked with them to create a solution that has helped them achieve immense customer satisfaction and loyalty.

The Perils of Presumption:

You could be spending endless hours testing your product, passing it around the organisation and beginning to think you have the hang of it, but reality could present itself as something else entirely. Out there in the world, it will be used by people from a wide range of geographical locations, cultures, professions and experiences with digital products. That kind of variety simply cannot be matched by the small sample within which you’d be testing before release. According to recent research by eTouchPoint, while 80% of companies believe they are providing great CX, only 8% of their customers agree.

And why should you be listening to your customers? Well:

• CX is projected to become the top brand differentiator by 2020, surpassing price and product
• Back in 2016, only 36% of the enterprises were competing on CX, the percentage today is 89 and the competition is primarily based on CX
• When it comes to choosing a brand, 88% of customers prefer one with a great customer service track record than one which flaunts innovative products
• 86% of US adults surveyed expressed their willingness to pay more for better customer experience

Therefore, what’s crucial is to figure out for whom the solution is. Is it for you? A specific target audience? Your distributors? Anyone and everyone? Once you’ve got that figured, how do you go about it? Simply put, the answer is build, learn and iterate till you perfect it. The learning being key as it helps you recognise pain points, eliminate them, release and learn more. The more data you collect and analyse, the more you know what is to be done to craft something your audience will really love. Also, the inferences will go a long way in helping you design the product to minimise bottlenecks and smoothen the customer journey.

We’ve been helping organisations multiply their businesses and elevate customer experience for a long time and have solved problems around meeting customer expectations and anticipating their needs in the feature planning stage. The key to all of this is effective collection and analysis of data. So what is data to be exact?

It is the comprehensive corpus of information on what your customers are doing at every stage of their journey through your solution. How are they interacting with it, at what times, at which points are they confused and dropping off, how many are actively using it on a daily basis, churn rates, how are they interacting with notifications, what’s the interaction like on offers and special sale events and much more. Once you augment your solution to send you information on each of them, you can draw up a strategy to fix the problems, push out rapid updates and improve your chances of success manifold.

A Transformation Story

We enabled Mann+Hummel to improve their filtering solutions with smart capabilities which gathered information on customer experience and based on that, added features which removed a bunch of manual tasks the customer had to perform. This delivered significant additional value to their customers, which naturally reflected in their revenues and reach. Their sales increased significantly and operational costs went down by a big margin. It all came back to using data to eliminate customer pain points as far as possible. Read all about it here.

Six Crucial Ways in which Data Benefits your Organisation:

• Provides Insights on Preferences: Data helps you understand usage patterns and customer preferences for you to deliver helpful tips and recommendations. Most customers prefer a digital solution that understands their needs and adapts itself to the various ways they use it. If you are in retail or running an online content platform, data enables you to recommend the right products, shows, books and so on. You could also leverage data to find out at what times the customers are active and run additional offers and promotions. Amid growing concerns over privacy, the only way to get customers to share data is to provide an experience which they enjoy as well as benefit from.
• Helps You Realise The Effectiveness of Your Solution: Speaking of digital properties, out of millions of apps in the market, less than 1% of them account for over 70% of use. Data shows you everything you are doing wrong as you analyse NPS (Net Promoter Scores), CES (Customer Effort Scores), CSAT (Customer Satisfaction Scores) and others. Look at the specific journey points where you are losing customers and deep dive into what’s damaging their experience at these points. Get a fixed version out as soon as you can and you never know, that could make all the difference.
• Enables you to Interact Better: Customers appreciate an organisation that engages them in dialogue, listens and acts on their feedback and consults them for new features and products. For one of our digital solutions, we identified a large group of users who were the most active in our social media platforms and created an advisory group with them. It resulted in a lot of positivity as they felt special and rewarded. A bunch of useful ideas were discussed and implemented, plus our viral marketing efforts received a boost as this group of highly engaged users did a lot to spread the word, resulting in the product’s continued success.
• Empowers You to Create an Informed Pipeline: When you know your customers and understand their expectations, you can get a lot of clarity on what your product pipeline should be for your target audience, how they should be designed for the best experience and what problems should you be solving on priority.
• Greater Customisation: It enables you to understand what type of products each customer is interested in so you can recommend more of such when customers open and explore your digital channel. Not having to search for specifics lends added seamlessness to the journey and customers enjoy faster, simplified checkouts
• Improved Personalisation: Knowing customer preferences, usage habits and purchase times can help you personalise the solution for each customer. Showing them special offers at times they use the digital channels the most, festival content they might be interested in, a customised landing page where they can see their potential favorites at a glance, personal offers on their special days and much more. This conveys to customers the fact that each of them are special and the company truly cares about providing them the best possible experience.

Conclusion

As product heads or transformation leaders, you might be owning the solutions and often believe you know best, but you are building them for an audience and looking for wide acceptance and recognition. You will not achieve that unless your users get an absolutely stellar experience, for which the offering must be intuitive, personal and secure. The key to getting it right is knowing all you can about your customers, and data is crucial in that regard. Embrace data, follow where it leads and your customers will be right beside you on the journey.

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The last few decades have seen a wave of technological revolution that has to a large extent changed the way we live and work. Many of the familiar devices and services delivered have become digital — from Internet-based banking, music, films, and shopping. In addition, social media sites empower us to interact with friends, family, and businesses in new ways. All of which leaves a data trail which is visible to companies that provide these services to leverage upon.

Enterprises need to ask the question: “ Is our business strategy still relevant in a digital world?” With the mass adoption of digital products and services,  consumers are generating huge volumes of personal data across all aspects of their lives, which is captured by organizations through digital channels or devices.

While data about a person’s friendship networks, hobbies and interests are captured on social media,  details of their shopping patterns are captured by online retailers. In addition to these established data sources, technological developments contribute to new sources of consumer data that provides unique insights into consumer behavior. We look into how businesses can harness and filter the insights from this information through social media analytics and digital analytics to drive innovation and improve customer experience.

Socialize Data and Share the Vision:

In most enterprises, the journey toward becoming more data-driven or data-informed is uneven. Enterprises need to move away from isolated data platforms, meant to support a business unit or role, to centralized platforms with multi-team access. This technology shift will create the required synergy with today’s cross-departmental, design-thinking collaboration strategies. Instead of teams maintaining their own work styles and dedicated discrete projects, enterprises need to form diverse teams from different groups and environments that share expertise and knowledge.

This type of culture aims to make the knowledge held by data and people hyper-collaborative and accessible across the company. Customer purchase histories, addresses, and persona, for example, aren’t available only for sales and marketing. Other business interest groups and project teams should access this valuable CRM data for their goals to leverage business and operations insights.

Data-Driven Cultures Work Together to Empower Teams

With data being available to all and free from silos, employees working in marketing, supply chain, finance, and other groups could have first-hand knowledge of the business processes of all the groups concerned. Their in-the-trenches expertise makes them the best source for engaging with the data and pushing it toward creating better business outcomes. Teams, working under a comprehensive data framework plan, need centralized analytics tools that let them interact with the data. A platform that enables assigned levels of access and role-based security keeps data-driven cultures within the defined framework outlined by the IT departments. Most importantly, this modern-day approach is faster than earlier models, for example in the past, a data query was sent to IT and a report would get generated weeks later. In the current scenario with data-driven culture, and constantly evolving technology lets a functional-expert query and wields the data to visualize the results immediately.

69% of outperforming organizations combine technology with business to innovate
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Executive Support Separates Data-Driven Cultures from Traditional Businesses

In a data-driven culture, leaders encourage employees to understand and interact with data for necessary action. Teams have the freedom to act upon, review, and respond to the results.  It’s plausible that not all results will be positive, and learning would come from both good and bad outcomes. What is critical, is feeding data into models and promoting an iterative process so that business decisions based on the outcomes are mature and precise over time.

In tandem with this support, data-driven leaders rely on dashboards in their day-to-day activities and they give credit to the data which is at the core of the business decisions and results achieved. This creates transparency with regards to change and shifts in the business. Since the irrefutable data serves as the role model, everyone in the organization get on board and begin to practice data to work, innovate and gain insight.

Realizing an Adaptive Enterprise

With changes underway, the possibilities are endless. Enterprises can enter into new markets, proceed with the desired demographic, or create business models around untapped assets. Data will reveal itself in unexpected ways, as businesses become more empathetic and aware of their customers’ needs. Under Armour, for example, created apps tied to Fitbit for its customers committed to exercise. Once an enterprise puts itself in its customers’ shoes, it can build service models and products that cut costs, create efficiencies, and improve customer engagement.

57% of outperforming organizations are good at translating insight into action
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This insight to empathy is only possible in an adaptive enterprise, which is derived from a data-driven culture. They respond to customers, partners, and market changes in real time based on insights from data. To stay ahead, they constantly ask:  Where are we now? What did we do today? Where are we headed?

Adaptive enterprises respond to those queries through a mixture of reports, that is more experimental and agile. They look at marketplace activities, including tests, trials, and theories that are revolutionizing the industry and causing disruptions. Adaptive enterprises assimilate these two modes to present a common view across the business. From this central platform, teams throughout the company can test and hypothesize, productize, and operationalize by manipulating the data. They run multiple tests and iterations, to pour the learnings back into the business.

Conclusion

Data is as vital as oil for the digital economy and is at the center of dictating insight-driven business transactions – from decision-making to cross-departmental collaboration. Businesses deal with plenty of data across the width and breadth, creating an opportunity to deliver insights and drive material outcomes to gain a competitive advantage. Reaching this inflection point starts with enterprises committing to redefining their businesses and creating data-driven cultures. This culture can spread when businesses establish three goals: break down data silos, engage teams through leadership and empower teams to explore data to drive better outcomes and insights that can be used to make a company’s offerings more relevant to its customers

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“There’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.” Erik Brynjolfsson and Andrew McAfee, The Second Machine Age1

In the new digital world, the interpretation of success is no longer linked primarily to efficiency, but to business agility. Enterprises need to seize the available digital opportunities in a rapidly changing business environment while acknowledging the needs of your technology-powered customers and acting quickly to successfully implement the digital strategy. At present, very few organizations are still operating through the hierarchical models, in which decisions are taken in a traditional top-down manner. Instead, organizations operating in the digital business orb have chosen loose hierarchies in which responsibility sits closer to the point of impact where each decision is felt. Also, enterprises are shifting the focus toward outcomes and away from the processes performed to achieve those outcomes.

Industry Trends

In order to succeed in this world of digital transformation, it is imperative that organizations must design a digitally connected and collaborative work atmosphere – or endure the risk of being left behind. Therefore, for the new digital workplace to thrive, it needs the employees to be equally aware. Employees have to start thinking on a new level in the non-physical world and see the enterprise as it should and could be, not only what it is currently. They should be cognizant of what has worked and has not worked in the digital world. Digital leaders also play a critical role in this as they need to be able to bestow confidence, build an environment of inclusiveness so that enterprises don’t fear to embark on new projects.

Since 2010, 13 million new jobs have been created, 30% of which requires high-level digital skills, which are filled with ease by millennials.
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Accountability Is Key

While you are on your marks and ready to create your strategy, it is necessary that you clearly define the roles of everyone who are set to be a part of the project. Leadership and management roles are particularly significant to discuss. In order to be successful, the aim is to keep every employee on the same page when it comes to strategic decision making. This will help to check the project from stalling in case of organizational or leadership changes. By precisely defining roles and responsibilities, a detailed digital strategy will reduce the impact of these changes in the days to come. By making sure that everyone is on the same page and placing strong support for the cause, this can prove to be priceless and ensures that an approved strategy moves forward without any further delay.

Set Appropriate Calls to Action & Measure Your KPI’s

As always, original content plays a big role in any well-thought-out digital strategy. But great content will never be enough on its own. It is important that you always remember the actual reason for building your site – and it isn’t to just fill it with a copy. You must decide what the purpose of your site is and what the content you’re creating is meant to do. Where are you trying to drive your visitors? What do you want them to do? You should be able to answer these questions and then create effective calls-to-action (CTAs) to drive them home. You need to put them in a position where they can easily make a purchase, register or call once they visit your website.

Along with generating powerful CTAs, it is critical that you are prepared to consider which key performance indicators (KPIs) you wish to measure moving forward. Choosing these carefully will allow you to effectively demonstrate the value that your digital strategy is providing to your organization. Both time and energy should be committed to this step. Failure to do so will make attributing successes of future projects much harder than they need to be. The strategy developed, will need to be tailored to the goals of your business and these components should be considered while planning. By including them into your game plan, your digital strategy will be up and running in no time.

New Organization Goals in the Context of Digital / Long Term Vision

New organizational/enterprise models also need a new attitude toward leadership. Leaders of connected teams in agile organizations require skills such as negotiation, resilience, and systems thinking. At times, the most seasoned leaders and business unit heads may not be the best fits to take charge of digital, agile, connected teams. Effective leaders in a connected environment must have a high degree of network intelligence to catch the drift of their enterprise, the industry and throughout the customer marketplace.

As connected enterprises continue to emerge, new tools are starting to make collaboration simpler.  Google Team Drives, Atlassian Confluence, Facebook’s Workplace, Slack, Microsoft Skype, and hundreds of others are helping to expedite the transition to networks of teams. Nearly 75% of enterprises are now experimenting with these tools—and benefiting in novel ways. For example, the Museum of Applied Arts and Sciences (MAAS) in Sydney runs three venues: Sydney Observatory, the Powerhouse Museum, and the Museum’s Discovery Centre in Castle Hill. Jira, an agile management tool,  is used for all aspects of the project (MAAS) — facilities, staffing, security, PR, and marketing, as well as digital education. By using HipChat, an auto distributor in Maine monitors tire pressures and repair items in its warehouses.

With digital being ubiquitous 55% of employees felt they could be more informed and engaged if they could communicate using a mobile app
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Short Term or Immediate Execution Plan

Take a moment to consider your target market. Odds are you are trying to reach a variety of consumers with diverse backgrounds, traits, and habits. Because of this, a one-size-fits-all strategic approach will do little to address the motivations driving each unique personality type.

Creating individual personas that describe your audience can help you make important decisions while building your strategy. Personas are an excellent way to identify the key target audiences you wish to attract while revealing specific goals for each one. The more research and effort you invest in defining your audiences, the more refined and effective your strategy will end up being. You should also consider performing a SWOT (strengths, weaknesses, opportunities, threats) analysis during this step.

How Digital Strategy Changes Internal Processes, Goals, and Priorities

To build the enterprise of the future, digital employees need “the capacity to find and delegate information to the right people without risking security. Unassuming communication with intelligent tools to support work processes will help to form teams quickly and work with networks outside their enterprise. A comprehensive, personalized and context-sensitive learning environment will build a fully functioning digital workplace and encourage employee engagement.  This is especially true if workers can access the digital workplace from different devices, which helps people work more efficiently on their own time. While the dimensions of space, capability, and intelligence are immersive and pervasive, this dimension helps to realize that the digital enterprise will embody more and more of what we need to do each day.

What Employees Can Do in Their Individual Capacity to Help in This Journey

The proliferation of information technology is revolutionizing the ways in which employees connect, communicate and collaborate.

This change accelerated over the last couple of years due to the development of three major trends:

• Evolving workforce: With the baby boomers about to retire, experience is leaving the company, reinforcing the need to capture it. On the other hand, the new generation of workers are very IT savvy and expect to have pliant, easy to use tools just as they have in their individual lives
• Data overload: while data is available and ever- growing at exponential rates, still  employees can’t find what they need, even with technological advancement
• Need for speed: With the lively pace of today’s work environment, employees need to work faster and collaborate more productively to get their jobs done

Due to rapid workplace demographic changes, employers strive to meet the changing needs of a multi-generational workforce.  As the availability and use of the Internet and mobile devices grows, the pace of change continues to accelerate. These changes are further intensified by open-ended demands to increase productivity and cut costs, making it tougher for employees to meet market expectations. Together, these bearings are reshaping the work environment.

The goal of digital strategy is to create relevant foundations for digital business. This means creating an enterprise that can pursue to reinvent itself as necessary to keep up with changes in technology and customer expectations. Digital strategy should be visionary enough to carry enterprises through shifts in the digital economy, in a way that continues to bring a digital edge to the business.

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