Data is central to businesses now. In today’s age of market volatility and complexities, businesses increasingly rely on data-driven insights, to make sense of their business environment, trends, and customers.
The drive to analytics is propelled by an increasingly data-driven world, brought about by digitalization and automation. Businesses collect data along with interaction and initiative they make. Digital clicks, social media, POS terminals, sensors, and a host of other sources enrich the enterprise with rich data. Customers leave their unique data fingerprint when interacting with the enterprise. Subjecting such aggregated data to analytics offers context to business actions, and allow executives to make smarter business decisions.
Data Analytics Improves Operational Decisions
Aggregating all related data enables the enterprise to make sense from thousands of product configurations, customer orders, service and support requests, bundled deals, and more. Enterprises may identify patterns from such chaos, to improve their operations, or focus more attention on combinations which deliver maximum gains.
Many enterprises leverage data to deliver better products and services to their customers. A retail business identifies the time of maximum patronage, to deploy more agents and close deals faster. A restaurant identifies the most in-demand dishes, to optimize the stock of raw materials, and avoid running out of such menu items. Hospitals analyze large amounts of structured and unstructured information in real-time, to provide lifesaving diagnosis or treatment options.
Big data analytics drives personalization as well. It enables interactions based on the personality and preferences of the customer, factoring in real-time location, buying habits, and other critical points to the engagement. Accenture estimates 75% of consumers more likely to buy from a retailer who recognizes their name and past purchases.
Data Analytics Improves Internal Efficiency
Data analytics improves employee productivity and overall enterprise efficiency. Analyzing data from a shop-floor, factory production line, or logistics supply chain unearth pain points, where the process gets struck for long. Likewise, real-time intelligence into deliveries, orders, and returns improve efficiency. The end-to-end view offered by data facilitates measurement of key metrics and continuous improvements on an ongoing basis.
Advanced analytical techniques improve field operations productivity and efficiency. Data is central to optimizing field operation schedules and making the process more responsive to customer needs.
Data analytics also paves the way for highly-accurate and efficient self-service tools, to cut down flab, and promote a lean enterprise.
Data Analytics is Now Central to Security
Of late, enterprises rely on data analytics in a big way, to improve security. They apply the background information discovered by data analytics for validations and predictions, to prevent fraud, authenticate users, discover security breaches, and in a host of other situations.
Effective deterrence depends on mechanisms to detect potentially fraudulent activity in real-time, anticipate future activity, and track perpetrators. Data analytics is tailor-made for such activities.
Using data analytics to authenticate users has commercial implications as well, not just security. A booking portal could scour data of the customer to find out if he is a serious buyer. Google and several other online service providers analyze real-time location data to check the authenticity of a log-in and go on to deliver location-specific ads. The opportunities are endless.
Data Analytics Aid Strategic Planning
Timely analysis of data also makes evident the changing times and trends. Enterprises act on such information to alter its products and service and stay relevant amidst the changing times. Data from the wider community shed insights on customer preferences, evolving macro-level trends, and other critical insights, for enterprises to tweak their product strategy.
Many businesses now apply data analytics to learn from failures. An analysis of data to understand what went wrong is invaluable to avoid being stung from the same hole twice.
A Deloitte survey reveals 49% of executives believing data analytics to improve their decision-making capabilities. Another 16% believe analytics enables key strategic initiatives. Two out of three respondents believe analytics is now critical to drive business strategy.
State of Data Analytics in Enterprises
Big Data is in vogue for some time now. Despite the obvious advantages of analytics, several practical, on-the-ground considerations hitherto impeded the adoption of analytics in enterprises. Many senior managers are uneasy or resent having their intuition replaced with analytics. Top management, faced with competing priorities for investment dollars, make trade-offs which may work against investment in analytics. The rank-and-file may fail to see the big picture and continue to work on gut instinct when faced with taking on-the-moment decisions or actions. As with any other change program, the workforce may resist co-opting analytics in work processes and decision-making.
The resistance to data analytics is now all but eroded through. A March 2017 research by Dun & Bradstreet and Forbes Insights indicate senior executives are finally giving data analytics its due, and making big investments in data analytics technology.
Also, data analytics, earlier restricted to IT and finance, has now spread across the enterprise, and cover the majority of business functions. Analytics is now widely applied in all gamut of enterprise functions, from the formation of the business strategy to powering operational excellence, and from compliance to advertising. Data analysts no longer work in a silo. In fact, analytics is no longer a specialist function. The emergence of advanced tools such as Salesforce Einstein makes available highly powerful data scientists to the common employee.
But even when all the above barriers are overcome or non-existent, there remains a pressing issue of talent crunch inhibiting the adoption of analytics. About 27% of analytics professionals find skills gap as a major impediment in their data initiatives.
For such reasons, many enterprises still have in place only rudimentary analytical technology. About 23% of analytics professionals still use spreadsheets as the primary tool for data analysis. About 19% of them use only basic data models and progression techniques. Many enterprises avail the services of third-party providers to support their analytic requirements, as a means to overcome the skills-gap. About 55% of enterprises outsource some or all of their analytics needs, to successful outcomes.
The Deloitte survey reveals 80% of enterprises already using analytics in some way or the other, and 96% of them revealing the importance of analytics will increase in the next three years.
As the first step to getting started, enterprises would do well to chalk out a sound Enterprise Data Model, which offers an integrated view of the data produced and consumed across the board, identifying the shareable and/or redundant data across functional and organizational boundaries. Such Integration of data offers a “single version of the truth,” minimizing data redundancy, disparity, and errors, and ensuring data accuracy, consistency, and quality. The enterprise also needs a sound Enterprise Data Strategy to harness such data for decision-making. They can then deploy the right analytical tools to leverage such data to good effect. Competent third-party providers aid the enterprise in all these tasks.
Data analytics is the new competitive differentiator. Business leaders who understand this fact and commit to the concept will succeed. Those who delay or ignore it, do so at their own risk.