Developing an Data analytics strategy that drives business transformationWatch
How to create strategy?
The business analytics strategy must allow the organization to reach its goals. It should enable transformation and innovation. Here is a 4 point plan to create a business analytics strategy that will allow the company to transform itself such that it reaches its planned business goals and improve upon them over a period of time.
1) Understand Real Requirements for potential transformation objectives
The organization must nail down the vision for the transformation and the key transformation objectives. The team should them elicit the real requirements that are to be built in order to achieve the transformation. For example, banks earlier had reports on customer data and his transactions. A bank can have the objective of customer stickiness. The bank will retain the customer in his entire financial lifecycle and maximise sale of financial products.
2) Get the Right Data and right architecture and manage them effectively
In order to transform business using business analytics, the basic component is to get the right data and manage it properly. The data derived has to be correct, consistent and updated. Everyone working on the business transformation should have the same data set. This will help in effective collaboration among different departments and facilitate good decision making. Tools for data storage, data analysis and retrieval have to be such that using data is efficient and they support the objectives of transformation.
Data architecture should be such that analytics capabilities can be leveraged well. You might need to extend infrastructure to improve reporting, analysis and forecasting capabilities. The right IT environment will help in discovering data relationships and draw new insights that can help the business. The architecture should ensure that the data is secure and private. Good data governance should be an underlying objective all the time.
For example, if a customer contacts the bank for opening an account but never really comes back to open the account, his data can be used and sent to the sales team who can contact the customer to persuade him to open the account. If a customer has balance overdue on credit card for some time or regularly, the bank can make an irresistible offer for a loan. Analytics can get such data to banks easily and if departments collaborate and manage data effectively, multi-channel customer relationship is possible.
3. Build models that predict and optimize business outcomes
Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes. More important, the most effective approach to building a model usually starts, not with the data, but with identifying a business opportunity and determining how the model can improve performance. We have found that such hypothesis-led modeling generates faster outcomes and roots models in practical data relationships that are more broadly understood by managers.
Remember, too, that any modeling exercise has inherent risk. Although advanced statistical methods indisputably make for better models, statistics experts sometimes design models that are too complex to be practical and may exhaust most organizations’ capabilities. Companies should repeatedly ask, “What’s the least complex model that would improve our performance?”
4. Transform your company’s capabilities
The lead concern senior executives express to us is that their managers don’t understand or trust big data–based models and, consequently, don’t use them.
Such problems often arise because of a mismatch between an organization’s existing culture and capabilities and emerging tactics to exploit analytics successfully. The new approaches either don’t align with how companies actually arrive at decisions or fail to provide a clear blueprint for realizing business goals. Tools seem to be designed for experts in modeling rather than for people on the front lines, and few managers find the models engaging enough to champion their use—a key failing if companies want the new methods to permeate the organization. Bottom line: using big data requires thoughtful organizational change, and three areas of action can get you there.
Develop business-relevant analytics that can be put to use
Many initial implementations of big data and analytics fail because they aren’t in sync with a company’s day-to-day processes and decision-making norms. Model designers need to understand the types of business judgments that managers make to align their actions with broader company goals. Conversations with frontline managers will ensure that analytics and tools complement existing decision processes, so companies can manage a range of trade-offs effectively.
Embed analytics in simple tools for the front lines
Managers need transparent methods for using the new models and algorithms on a daily basis. By necessity, terabytes of data and sophisticated modeling are required to sharpen marketing, risk management, and operations. The key is to separate the statistics experts and software developers from the managers who use the data-driven insights. The goal: to give frontline managers intuitive tools and interfaces that help them with their jobs.