Power - Belonging
Having a clear enterprise strategy is an important foundational step for managing and extracting value from all data. Clarity around data management and governance, and a clear direction about what an organization wants to accomplish with data-driven insight, are both crucial. While data analysts play an important role, they need to be directed towards specific tasks and goals. That calls for the creation of data teams operating under a well-defined strategy and data governance structure to bring it all together.
Companies that want to transform data into value often create cross-functional teams that include data analysts, business architects, marketing strategists and data-loving businesspeople who can identify the problems that data might solve.
The greatest return on data will come when analytics is available throughout the enterprise. Indeed, the importance of putting data and analytics in the hands of the employees in order to drive better outcomes is definite. However, this is an area where many companies also struggle. When asking managers if they are ready to democratize their data so it can be used by anyone, the answer is almost always no.
But at the same time, they want data and analytics to be used in every part of the organization where it can deliver value, which is everywhere. Data analysts are always jealous of the data and obsessed with the idea of data hackers. In reality, there are way more advantages to data democratization, then there are risks of data hacking. The latter may appear even without giving data access to the team.
What’s holding organizations back from making more investments in data collection, automation and analysis that ultimately enable them to derive more value from their data? Data is a precious resource, but for many companies it is sitting in the “warehouse” waiting to be processed in order to provide value. It’s important to measure and report on business outcomes of their data and analytics investments. Many organizations have difficulties accomplishing that.
When people think about identifying business values, they want to say “we did this and got that”, but that’s difficult to do unless you have structured a specific inquiry around data to let’s say optimize a marketing campaign. The connection between data, analytics, and business outcomes can be fairly abstract. An operational decision based on data analytics or a strategic decision about entering a new market that has a long-term payoff can be more difficult to calculate.
That will require a combination of qualitative and quantitative analysis to assess the quality of decisions made. There is a tendency in taking the data and connecting it directly to higher revenues or profit, but leaves a gap in between. It is important to measure the impact on one’s decisions. Are you making them faster or better? Did you have the right analytics to make them? If your answer is yes, keep investing in data collection and analysis.
Analyzing the same data over and over again with the same formula is tedious and boring. But only persistent, patient analysis finds the real insights hidden in the data. It is necessary for the data to be analyzed in the same direction over time, maintaining the main strategy and logic of the analysis. The best way to solve this problem is automation. It is proved that looking at the same indicators every day gives birth to a business idea that can change the path to success
All organizations will need to prioritize the data-to-value, if they want to stay competitive for the years ahead. The velocity of change is only going to accelerate in the digital age. Developing a strategy and platform for transforming data into value and involving the whole team to use that data, is how businesses can keep pace with the existing rate of innovation and change.