Why Analytics is central in enterprise Digital Transformation - Sciera
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Why Analytics is central in enterprise Digital Transformation

Digital transformation is a widely circulatedtermthat has taken the industry by storm. It seems like every organization wants to jump on the bandwagon to reap its benefits. But the term has been pretty loosely used currently and the core concept has become somewhatfuzzy.

So, a baseline explanation first. At its simplest, Digital transformation essentially means leveraging digital technology to improve the bottom line. This can be done by changing operational processes, altering business models, and automating workflows.

The industryoften uses the terminology SMAC (Social Media, Mobile, Analytics and Cloud), which is an acronym naming the main pillars of Digital Transformation. At the risk of sounding biased we would like to believe that of the four, it is analytics that is the most crucial for Digital Transformation.

This can be substantiated by a view into the benefits enterprises are looking to extract fromDigital Transformation. Essentially digital technologies are being leveraged to improve customer experience, streamline operations, marketing automation, etc. Blockchain, IoT, NLP, Deep learning are powering data-based decision-making that helps enterprises achieve these ends. The use cases and the supporting technology elements make it pretty apparent that this story is all about analytics. Analytics is the underlying engine powering all the use cases.

Data has already been categorized as an organizational asset which can be used for making strategic decisions. According to Gartner, by 2022, 90 % of the organizations will start using data as a core input for decision-making. Already 69% of the organizations are using data from newer sources to understand their market. 64% of organizations have started using predictive and other forms of advanced analytics in their business. 67% are exploring new types of analytics to consume the data they have with them. But what’s turning that data into insights?

The most common types of analytics include Descriptive, Prescriptive, and Predictive analytics. Descriptive includes day-to-day or operational reporting. Prescriptive analytics helps in coming up with recommendations. Predictive, as the name suggests, helps in predicting the future so that the organizations can reap maximum benefit out of it.

The major driver for Analytics-driven Digital Transformation has been the ease with which an organization can now gather and store data. Enterprises can store petabyte-scale data economically on the Cloud. Their ability to process such huge amounts of data too has increased. There are tools in the market which can crunch these large amounts of data and come up with intelligent insights in a flash.

Most of the data being fed into the analytics algorithmsis unstructured data. Modern tools have the capability of cleansing that data and make it analytics-ready. Encouraged by this ease, enterprises have already started pulling in data from newer sources like social media and video sharing platforms. This ishelping them accelerate meaningful Digital Transformation.

Let’s look into a couple of examples.

The most ubiquitous example is of course Amazon. This is the Digitally Transformed avatar of the age-old eCommerce portal. The landing page for Amazon is different for each user and gets organized based on the most-searched items. There is a tremendous amount of data-crunching that takes place behind the curtains to get that specific a view of the customer. What can be her likes and dislikes definethe group shegets clustered in. A similar logic is applied for the recommendation engines. For example, if you buy fantasy books then watch out for more books, movies, memorabilia, and musicwill show up as recommendations by the site. Amazon analyzesnot just historical data, but what people with similar choices have bought, and a variety of other indicators to come up with a solution that maximizes the chances of the user clicking on “buy”.

This application of the recommendation engine can be seen on YouTube as well as Netflix where the driver is providing a winning customer experience that maximizes usage and prevents churn. Do suggested videos ring a bell? Given your history, favorite videos and recommendation are placed. Decision treesare applied to understand the probable paths for a viewer and what she might want to watch next.

Other industries tooare following suit with optimizing the screen or landing pages basedon customer click-through rates. Banking apps have been pioneers for such an application of data analytics. The modules which are used by customers the most will be shown on their screen. Why see a money-transfer option if you never transfer money?

Digital technologies like IoT are being used to make manufacturing leaner. But the sensors are only as useful as the data they generate. And the data, only as good as the analytics it drives. The data from sensors is studied to understand whether a production line is operating successfully, machines need to be serviced, and material to be procured to keep the manufacturing line moving. Similarly, even the supply chain is being streamlined by analyzing data from GPS systems. Analytics-driven fleet management is today’s reality which has helped logistics companies become cost-effective and serve their customers effectively.

Even with all that, we are just scratching the surface. Technologies like blockchain will soon become mainstream.  We will see more interesting usages of Deep Learning and Machine Learning in the near future. The underlying data foundation is in place and Analytics has shown the way to these realities.In conclusion, we must stress that the primary purpose of Digital Transformation is to reach out to more customers and increase conversions. That can be effectively powered by data analytics. And that will be the greatest impact of Digital Transformation.