Data collection and data storage is not something which can take companies miles ahead than the competition. Disconnected & fragmented data is something which cannot lead to comprehendible visualization of data, as they tend to reside in desperate segments and in a detached state or in isolated buckets.
Left disintegrated, fresh approach to turn it into cohesive and compatible data sets is an added challenge that companies are required to address. This is an effort to answer the usual question that with humongous data in hand, why is it that we keep on making bad business decisions. Enlisted are five reasons why more data does not guarantee better business decisions.
1. Data can be used for better informed decisions, not better decisions
It’s time it is understood that there is a huge difference in better-informed decisions and better decisions. Better informed leaders do not necessarily make better decisions, but better decisions usually are made by better-informed leaders. A humongous amount of data does not suffice always. Different and disparate variables will certainly shape the path and the final decision. History has witnessed instances, where leaders equipped with huge amount of data to support their decision-making process.
2. Technology with huge amount of data not fit to deliver better decisions
No way are we trying to negate the importance of technology. When clubbed with the right kind of technology, analysts deliver business intelligence for:
- Insight into the right data
- For the right role
- The right time
The human intervention of expert data analytics teams & decision analysts plays a pivotal role. Their passion, irrespective of the challenges they encounter or scarcity of resources, or cleansing required for structured or unstructured data; is the determining factor.
3. Lack of direction and consistency due to lack of vision
Everything starts and ends with vision, the one set by the leadership team. A lot of things depend on whether they are inclined towards promoting a data-driven culture for data-driven decision making or not. The success of the entire data analytics initiative depend the most on this one attitude.
When the vision is set, data-driven leaders will not hold back to lead by example. They will not only consume the data for analytics but also would ensure to apply the insights derived from these data assets to decisions which matter. Their efforts are aimed at demonstrating the power and advantages of data and analytics for the rest of their teams. By recognizing data as a strategic asset, they provide a clear and consistent message for everyone to follow.
4. Data needs a strategy
Steps to deliver reports based on more data or big data; you think is it synonymous to better-informed decision making? If yes, you are highly mistaken.
For delivering the true business value of business data & attain data analytics maturity, companies should follow some of the established best practices. Enterprises are required to formulate a carefully thought out enterprise data strategy. The existence of data intelligence framework, independent of organizations business or technology strategy is negated.
5. Ask smart questions
Yes, you read it right. Asking smart questions for data analytics grows your business. With so much of data in the repository, if you are not asking the right business questions or simply dont understand the assumptions; doesnt do much good.
The next walks in critical thinking, one of the most demanded skills in business. Maybe that is the reason why it is taught more of at every level of higher education. Examining evidence based on solid data that is relevant to the question at stake before concluding or deciding is more than necessary. This is the only way to realize the promise of more data-to deliver actionable insights for not only faster but better-informed decisions.
Unlike past decades, today businesses of all sizes are set to capture huge data, both structured and unstructured, at really competitive rates.
Simultaneously, advanced technologies including cloud, mobile, big-data, in-memory computing, IoT, artificial intelligence and machine learning are powering its growth which makes it easier to acquire and analyze different data points. If insight from enterprise data engines is going to enable organizations to drive growth and profitability, data and data experts or data analysts must become a conduit to enable faster and better-informed decision-making.