In many ways, the digital economy that we are so acquainted with today began when businesses started shifting into a data-driven decision culture from their traditional operation model where leadership decisions were taken using gut feelings and biased recommendations.
McKinsey estimates that enterprises that leverage analytics on their data are 19 times more profitable than those who are still distancing away from it. Also, businesses that use analytics for their customer acquisition strategies can boost their success rate by over 23 times!
Gartner also found that nearly 90% of successful businesses will consider data and analytics as a core asset and competence differentiator in the market.
The possibilities of data analytics are immense. It will be rather embarrassing to again put out generic content that explains why analytics is important for businesses of all sizes. Nearly every organization has an understanding of what analytics can do in its business model. From boosting profitability to improving productivity, eliminating inefficiencies, and bolstering customer satisfaction through personalized services, there are plenty of use cases that a business can pick and run pilot analytical programs.
For businesses, the real challenge is not to understand why analytics is important but how to get their analytics implementation right. Recent studies show that only 48% of firms agree that they compete on data and analytics even though nearly everyone invests in analytics.
This challenge exists because most organizations do not have a clear roadmap on getting their analytics implementation right the first time. Big data investments are not cheap. Cloud and subscription services may create some space for a breather but considering the widespread use-cases that exist for an analytics implementation, an enterprise clearly cannot afford to make big mistakes with big data.
Let us explore five tips for a business to reduce risk and have their analytics implementation done right.
Analytics is often a means of exploring deeper characteristics of a business to discover hidden insights. But before beginning the analytics initiatives, it is important to first identify whether the problem statement or opportunity that is being dissected for analytical processing is a feasible one for the business. This is where the exploratory angle of analytics comes into the picture. Businesses need to first explore and discover areas where there is a large hotbed of data lying untapped. Once it is identified and vetted for potential, further analytical processing and deeper insight generation can happen. In other words, exploiting an opportunity should take place only after there is an exploratory phase to gauge its potential. There needs to be a valid business outcome tied to the scenario being analyzed. Analytics being a high-stakes, high-expense game, cannot afford to gamble with luck.
Data analytics brings in a fresh perspective for running business operations based on facts and figures. Hence, for such an initiative to work well after implementation, it is important to ensure that all stakeholders that contribute to the program are aware of how to follow a data-driven culture for their daily routine. The skills needed for managing large data sets, complying with data-driven operational models, dealing with customers and business dynamics after being made aware of risks or changes through analytical forecasting, are all new experiences for your employees and partners. It is important to emphasize training and learning programs to grow awareness about following a path tailored by data science rather than raw human intelligence.
Every business has its own unique operational footprint. Hence, it is important to have the right metrics tracked, the proper data flow established, and the most tailored data-modeling approach followed for best results in analytics. Ensuring consistency in data supply, entertaining dimensional diversity for different data sets, and covering end-to-end transactional experience for data points are important steps in creating a unique data pathway for analytics.
Analytics initiatives are often the equivalent of changing the way your brain makes decisions for your body. It can give a completely new meaning to perceptions using data-driven computational processing. Hence, going for all-out end-to-end implementation of data analytics practice for the entire business is not a wise move. It needs to be done incrementally and by carefully evaluating success rates and progress metrics for approaches taken. The best option is to start with a proof of concept for a pilot initiative. Take a smaller business case and find out how leveraging analytics helps in unlocking value for that particular case. Ideally, it is easier, to begin with, the analytical processing of data for a metric that is already being tracked by the business using traditional manual intensive processes. By comparing the results, it will be easy to see how fast and better, the analytical approach helped to achieve the same metric.
It is true that analytical initiatives are less complex when they deal with structured data sets. However, in reality, a business needs to be always prepared to deal with uncertainties. Take, for example, the COVID-19 pandemic. It became one of the biggest single-event disruptors of business and consumer preferences in modern world history. As analytical models must deal with real-life data to make predictions and deliver insights, it is best to expand and include all facets of operational data in your business and not just structured data. It might take efforts to cleanse and organize the data initially, but strategically covering all data points surely helps in delivering more accurate analytical insights.
By 2030, it is estimated that the market size for analytics will be worth nearly USD 684.12 billion globally. It will be a key pillar of driving transformational change in business operations for enterprises big and small alike. Getting the analytics implementation right is crucial to ensure a more competitive advantage in the market. For this, enterprises need more than just a thorough knowledge of the best practices mentioned in this blog. They need practical advisory and guidance on a wide range of scenarios from picking the right technology infrastructure to following the right data modeling.
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