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The only discussion anyone can have about big data today is not “how?” but “how much?” Essentially, the volumes belie all expectations. It is expected that by 2025, we will be creating 463 Zettabytes of data every day. With this massive data, one would expect significant numbers of insights. But that’s not always the case, given that almost half of the collected data is unstructured, often misrepresented, wrongly recorded or even indirect.
While this could be an invaluable source of insights and the foundation of intelligent enterprise and , there is much to be done before we get there! This is where has the ropes. But, enterprises must look for pitfalls when using analytics for successful strategizing.
Globally, data integration and collation has been challenging exercise. The reasons center around two things — lack of skills and tools to integrate complex and diverse interfaces that access data from the market. This means that most organizations that do massive data collation need more technology support and skills to analyze that data.
We often overlook this crucial step between and insight extraction. Studies clearly say that while data is collected in bulk, more than half of it is unusable for various reasons ranging from verification and validation to landing with outdated, inconsistent and irrelevant data. This is the third challenge to deriving the best possible insights. The success rate of analytics function depends on these three — the basic need for good and clean data, good integration with data collation tools and skills to recognize and identify patterns in the context of the target market.
When an enterprise can balance these three factors, it can expect insightful results through descriptive, predictive and prescriptive analysis using its analytics tools.
Role of analytics
This then begs the question: Is a good analytics foundation so simple? Can this tripod hold up extensive and intensive strategies? Is that all to it, or is something still missing?
In the post-pandemic world, all dynamics seem to have changed. Customer behavior is unpredictable, and so are business decisions. Some businesses have been irreparably impacted, while others have taken wing under pressure. Nothing is as it was two years ago. Privacy parameters are changing, cookies are out and newer ways of data collation are being used. Some are succeeding; some are not.
The entire paradigm is unstable, on the verge of breakthroughs, disrupting data insights and the collation process as we have always known it. Too much is constantly in a state of flux. Under the circumstances, stable, conventional processes and methods cannot succeed unequivocally.
One of the biggest concerns in data analytics is security risks. As analytics becomes increasingly AI-driven, the fear of biases looms large. With rising data privacy concerns and regulations, accessing clean data in significant volumes is becoming increasingly challenging. So now, there is ample data but not enough clean data. Integration could pose security risks, so analytics tools must be securely scooped in. All this is for data that still needs more security and clarity. Even as ML and AI play an increasing role in data processes, regulations and compliances are becoming more stringent.
Finally, but not at the end of the line, there is a drastically changed purchasing power disparity, buying behavior and preferences from the consumer perspective. All of these uncertainties are making success increasingly challenging to measure or maintain. The scenario will become even more challenging with the death of third-party cookies.
What’s up next
The post-pandemic world will be different from what anyone predicted four years ago. And yet, data will need to be the mainstay of all strategies. AI, despite its biases, is fast becoming an essential part of analytics if Data has to make any sense.
Experts are also recommending that the way forward could be to collate just enough data to generate predictive insights instead of gargantuan byte datasets. It will be all about going deeper rather than going wider.
To work smarter without the cookies, many brands opt for first-party data, which not only gives them customer data but also helps to establish very special relationships. This has to be the way forward because historical data is no longer helpful, and their insights no longer hold. The two years of the pandemic have crumbled all edifices of planned strategy and the base of insights the older data provided.
Now we will need to start from ground zero. In the new world, analytics will need to be transparent; all data will be open to scrutiny and must be collated using secured and reliable tools. This approach is not radically different from what was happening pre-pandemic, but now it will have the added value of higher reliability and perhaps better accuracy. A fundamental change that will be needed is enabling the sharing of data and hence its analysis across teams. This will ensure that all insights are leveraged by the entire organization and drive cohesiveness in data-driven decisions, becoming a better tool to meet business objectives. Companies will also need to make their analytics approach more agile to changes in market dynamics, and as long as it remains, the value of the analytics tool stays. The company’s leadership plays a very critical role in this brave new world of open data analytics — if stakeholders understand the value of this transformation, encourage new lines of analysis and drive new ideas for insights, it will lead to much better outcomes.
As long as the data collated is relevant to current market preferences, its analysis will also be contextual to current market dynamics. That is when the power of analytics will be harnessed for enterprises to understand and leverage deep domain insights that can drive a new breed of market strategies- relevant, helpful, and successful.