The Data (R)Evolution

The global landscape of Data and Analytics is evolving rapidly. Here’s what you need to know.

Of the plethora of revelations that the COVID-19 pandemic has brought to global businesses, one major aspect is this: for organisations making use of traditional analytics techniques based on large volumes of historical data, several models were found to be rather irrelevant, rendering much of the data useless. Therefore, one of the changes that several (forward-looking) analytics teams worldwide are undergoing now involve pivoting from traditional ‘big’ data AI techniques to analytics that require lesser, or ‘small’ and more varied datasets.

Source: Gartner

This is, of course, just one of the several trends that Consulting giant Gartner outlines as being forerunners for Data and Analytics teams in the coming years. Overall, they have clubbed trends under the following (main) headers: (i) Accelerated Change in analytics through more effective AI and data sources; (ii) Operationalising business value using more effective XOps and (iii) Flexible storytelling for a wider audience using insights from data. According to Gartner, such trends can “help organizations and society deal with disruptive change, radical uncertainty and the opportunities they bring.”

I.        Accelerating Change

Leveraging innovations in Artificial Intelligence technologies, the science of data analytics is set to become much more composite, agile, and efficient in integrating diverse data sources.

• Smarter, more ethical AI: Smarter scalable AI will not only improve learning algorithms but also increase efficiency and reduce time-to-value. Small data techniques and adaptive machine learning will thus form the pillars of a more responsible and ethical AI.
• Composable analytics on the Data fabric architecture: Components from multiple data, analytics and AI solutions will be used in conjunction for a much more flexible and user-friendly experience through the use of the smarter data fabric architecture.According to Gartner: “Data fabric reduces time for integration design by 30%, deployment by 30% and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles. Plus, data fabrics can leverage existing skills and technologies from data hubs, data lakes and data warehouses while also introducing new approaches and tools for the future.”
• Small and wide data: The increasing number of challenges posed by the complexities of AI and scarce data sources will be tackled using small and wide data. Small data will be used to develop newer models with lesser data but similar useful insights, whilst “wide data — leveraging “X analytics” techniques — enables the analysis and synergy of a variety of small and varied (wide), unstructured and structured data sources to enhance contextual awareness and decisions”
  II.     Operationalising Business Value

Enabling improvements in decision-making and making the transformation of data into analytics an integral part of business processes is set to be key in operationalising business value for firms, through the use of more effective XOps.

• XOps: The objective of the XOps landscape (including aspects like data, modelling, machine learning, platform etc) is to reduce redundancy and achieve scalability using DevOps best practices. This will ensure reliability and reusability while enabling automation and reducing technology and process duplication in flexibly designed and governed decision-making systems.
• Decision Intelligence: Engineering decision intelligence (including complex adaptive system applications, AI and conventional analytics) in congruence with data fabric to enable organisations to gain insights quickly and drive business processes more accurately and repeatably.
• Data Analytics as a core business function: The indomitable significance of data and analytics to accelerate business initiatives will no more remain a secondary focus — becoming a primary core function instead. Research has shown, that if Chief Data Officers (CDOs) are used in setting up business strategies, business value can rise by a factor of almost 2.6x.
  

  III.   Distributed Everything

Digital storytelling through the flexible relating of data and insights in order to reach and empower an even wider audience will be central to the future of businesses.

• Everyday Graphs: Graphs form the foundation of modern data and analytics allowing for improved collaboration between business verticals and analytics, and businesses have started recognising that. In fact, almost 50% of Gartner inquiries surrounding AI follows a discussion around graph technology.
• The Augmented Consumer: “Traditionally, business users were restricted to predefined dashboards and manual data exploration. Often, this meant data and analytics dashboards were restricted to data analysts or citizen data scientists exploring predefined questions.

However, Gartner believes that, moving forward, these dashboards will be replaced with automated, conversational, mobile and dynamically generated insights customized to a user’s needs and delivered to their point of consumption. This shifts the insight knowledge from a handful of data experts to anyone in the organization.”

• The Edge: As more and more data technologies move outside traditional data centre and cloud environments, latency for data-centric solutions will be reduced, enabling greater real-time value. Moving analytics to the edge will allow data teams “to scale capabilities and extend impact into different parts of the business. It can also provide solutions for situations where data can’t be removed from specific geographies for legal or regulatory reasons.”