Why analytics technology transformation is crucial for enterprises today?

Author – Jagat Pal Singh, CTO, Cybage

Does your analytics technology need an upgrade?

Analytics is driven by two key forces, the first and foremost is the goal of analytics and that is better decision making. Whether it is about product roadmap, target market, process change, investment, geography or any other focus area of an enterprise, it all boils down to better decision making. The second force is data itself; you can only analyse data you have access to in order to make decisions.

Both these forces are changing at a rapid pace—data has transformed and continues to transform, at the same time, the nature of the decision and the speed of decisions. It’s time that the analytics technologies of an enterprise catch up with both these realities. In this article, we will assume that the enterprise is already convinced on the need to leverage data for decision making and that personalization of experience—whether it is of a customer or an employee—is no more a distinction but a necessity for the business to survive and thrive.

The nature of data has changed in three dimensions commonly referred to as the three Vs—Volume, Velocity, and Variability.

• The volume of data: According to Gartner, 69 to 70% of enterprises mention data growth as their top concern. ESG’s research points out an unsustainability, where 63% of surveyed organizations report 20 per cent or higher annual rates of data growth, yet the average annual growth in IT budgets is a mere 4 per cent (source: igneous.io). As stated by IBM, 2.5 quintillion bytes of data are created every day; in 2016, 90% of the world’s data ever was created in just 2 years! With digital adoption by enterprises and customers alike, data growth is rapid and will only go up from here. The important question then is: is your enterprise storage technology ready to scale to this rapid growth? Is it going to be vertical or horizontal? At what price point? The answer to these questions culminates in your technology choices.

• The velocity of data: The rate at which data is created by customers and enterprises has risen exponentially. Whether it is social media streams, campaign performance, customer interactions with digital channels, machine-generated data within the enterprise or the customer-facing infrastructure, all pump in data at massive rates. The Internet of Things (IoT) is real for most businesses. Is your enterprise technology ready to not only accept these firehoses of data but to also process them at the required pace? Often, the focus is on ingesting and storing the data for future use only to find out that the technology choice was not complete when it came to processing this high-velocity data.

• Variability: Enterprises not only have to deal with multiple sources of data but they now also have to deal with a large amount of unstructured data such as photos, videos and social media posts. These have hidden insights related to the market, customer behaviour, competition and so on. Is your analytics technology tuned to not only process and extract this information, but to also correlate this across multiple channels and come up with valuable actionable insights that can affect your top line and bottom line and enable you to define new business models, new products, and market segments?

Traditional vertical thinking for scalability may not work on these new breeds of data sources and data itself. Traditional models of data processing through warehouses and offline jobs may no more match the speed of your business. These are shifts that are already on top transformation lists of large enterprises and others will have to follow suit before their competition embraces them.

The nature of decision making is changing. The speed at which decisions are to be made and the transference between these decisions—the impact of a decision made in one department or business unit may have an adverse or positive impact on another. Enterprises have been forced to become more dynamic and agile to keep with the dynamics of the market and competition.

• The speed of decision making (Velocity): An agile enterprise requires quick decision making not only to save precious time but to also prevent loss of opportunity or onset of threats. Product price points are being revised every day if not more often, campaigns and discounts are an everyday affair, customer and employee engagement are no more periodic activities, vendor insights, supply and demand require continual decision making across organizational functions. Is my enterprise analytics technology helping me build a data-driven culture? Does it support the speed of decision making few times a day, if not more?

• Augmented decision making: The dynamics and data are overwhelming and growing beyond the processing capabilities of decision makers in the traditional analytics lens. Can your analytics technology bring in self-learnability (adaptive) prescriptions to these decision makers to not only enhance the speed but to also uncover threats and opportunities which are hidden under massive layers of organizational data?

• Analytics consumer breed: While business analysts still play a crucial role in consuming the reports and insights for enterprises, the need for agility has pushed decision-makers at all levels to also resort to self-help. Is your analytics technology adjusting itself to cater to these SMEs who are not traditional data churners?

Control and compliance is another critical dimension that forces enterprises to revisit its analytics technologies. While on one hand encouraging data-driven decision making across the organization in need of the hour, on the other hand, authorities tightening the regulations and compliances pose a huge challenge.

All of this poses a crucial question: is your enterprise’s analytics technology ready to handle new-generation data? Can it serve your new generation analytics consumers’ needs? Does it safeguard your enterprise against data threats and compliances?

It is most likely that your answer would be ‘No’, and the transformation of analytics technology is extremely crucial in this case. And if your answer is ‘Yes’, you are a visionary in the digital era, and you must take a bow for being on top of the curve.