Given the noise around data analytics, many companies have woken up to perceived benefits of being an analytical organization. Many of them want to transition into an analytical organization. Unfortunately, a big-bang approach is neither desirable nor possible in analytical capability building. We should look at the exercise of the analytical building as a continuum. It is extremely hard to leapfrog.
Companies that are at the bottom of analytical capability might have some data but they do not actively use this data for decision making. The first building block towards analytical organization is the quality of the data. If the quality of data is suspect, the first effort should be to improve the quality of data. In many situations, the organization might have to build systems that collect data. For example, an organization which has all the customer data in a non-digital form might start digitizing all the data and then realize that digitization has resulted in inaccurate data. The best approach for them might be to first build systems for that function and wait for new data to be collected through continuous operations before embarking on any type of analytics.
If the organization has quality data available in specific functions and local management in these functions wants to leverage analytics, they can embark on the path. It is extremely important to understand that even in this scenario, the support of local management is critical. If the management believes that because of them being in the place for years they know everything and they can take decisions themselves, the organization would never embark on the path to be an analytical organization.
If the organization is using data-driven decision making in some of the functions and then the leadership team is convinced that they need to move on the path to be an analytical organization, they need to assess the state of Organization, Skills, and Technology to evaluate the path they might take to proceed further. At this time the organizations would need to ask the following questions.
Companies that are at the bottom of analytical capability might have some data but they do not actively use this data for decision making. The first building block towards analytical organization is the quality of the data. If the quality of data is suspect, the first effort should be to improve the quality of data. In many situations, the organization might have to build systems that collect data. For example, an organization which has all the customer data in a non-digital form might start digitizing all the data and then realize that digitization has resulted in inaccurate data. The best approach for them might be to first build systems for that function and wait for new data to be collected through continuous operations before embarking on any type of analytics.
If the organization has quality data available in specific functions and local management in these functions wants to leverage analytics, they can embark on the path. It is extremely important to understand that even in this scenario, the support of local management is critical. If the management believes that because of them being in the place for years they know everything and they can take decisions themselves, the organization would never embark on the path to be an analytical organization.
If the organization is using data-driven decision making in some of the functions and then the leadership team is convinced that they need to move on the path to be an analytical organization, they need to assess the state of Organization, Skills, and Technology to evaluate the path they might take to proceed further. At this time the organizations would need to ask the following questions.
- What is the existing capability of the organization that might help the journey towards an analytical organization
- Which key processes and decision will help most with the data-driven decision making
- What is the differentiating factor of the organization
Once an organization decides to embark on increasing their maturity on analytics continuum, they need to choose a path. If there is an extremely high commitment from the leadership of the company, the organization can invest and proceed on a path toward a big bank approach towards becoming an analytical organization. An organization may start one or many of the following activities.
- Find opportunities to collect new data and improve the quality of data
- Build a relationship across different datasets
- Build data pipelines
- Build processes and governance organizations
The second path that an organization may take is a proof of concept path where specific problems are picked and a proof of concept is performed before it is rolled out the larger organization. This is a low risk, low reward, high cycle time option.
The point to understand is, a big-bang approach would only work if the top leadership of the organization is fully committed behind the initiative. The PoC approach can be undertaken with finding a functional manager as a sponsor but may result in multiple systems that don't work together.
