Before we get into ‘How to start using Analytics’, let us take a step back and formalize the definition of Analytics:
Analytics is all about using data to identify trends, validate hypotheses, and predict future outcomes. Communication of results through charts and graphs is a part of data visualizations.
In the social sector, analytics can predict kids who are more probable to drop out from school, as a result of which an NGO can focus on those kids and make a much more effective impact. Similarly, in the banking sector analytics can find out the target set of prospects for cross-selling a particular product, thereby reducing the marketing cost for the bank. You can check some more case studies.
With all the buzz around data & big data technologies these days, it is very easy to go overboard – without you and your organization being ready to unlock the true potential of data. We begin our blog series with a 10-step guide to using analytics:
- Define the problem: The first thing you need to do is identify the problems you want to solve using analytics. You should know that analytics is not a magic wand that will make all your problems disappear. Data can answer almost all questions – but for that to happen, you need to ask the right questions.
- Analytics cannot replace choice: One of the reasons analytics can never be 100% machine-driven is that decision making is always a selection between choices – that can at best be rank-ordered, but there would always be an element of philosophy, reason, experience, future plans in making a selection of choice today.
- Estimate Analytics Return on Investment (RoI): Estimate the amount of benefits that you expect to draw from analytics. It is not always easy to express the benefits of analytics in terms of revenue. You need to identify the right metrics to communicate the benefits of analytics.
- Build a roadmap: Once you understand the benefits of analytics, develop a clear roadmap of what you want to achieve with analytics, and how you want to achieve those goals – whether you want to build an in-house team, or you want your analytics tasks to be completely outsourced. You could also choose a combination of the above two.
- Siloed or Integrated: You also need to define whether your organization is going to pursue a siloed approach to implementing analytics or whether you want to view it as an organization-wide effort. Both the approaches have their pros and cons, and a rational decision, which works best for your organization, needs to be taken.
- Data Governance: Define clear data governance rules, including data ownership, architecture, policies, data quality, rules for resolving data related issues and policies for data management. Depending on your operations, you might have access to private data, which needs to be protected. Proper data security, confidentiality and access rules need to be defined.
- Graphical Analysis: A lot can be inferred by looking at simple one-dimensional graphs and cross-tabulations. Simple graphs can help you spot anomalies or identify trends. In the age of big data, do not ignore the power of simpler analytical methods.
- Identify experts: Not all people are apt in handling and interpreting data. Identify people within your organization who are more comfortable than others in handling data. Analytics is always contextual, and the more experienced a person is within your organization, the better s/he can contextualize analytics.
- Engage Analytics Consultants: While it is a nice idea to use in-house expertise, asking for external help can enable you identify the best way forward to solve a problem analytically. Even if you have sufficient expertise, sometimes, having an external perspective helps you see things differently.
- Test, Learn & Modify: Any successful strategy needs to evolve with time. The same is true for analytics. You should not expect to use the same techniques or the same model over and over again. The problems need to be revisited over time for getting the best return out of your analytical exercise.
Based on your experience of implementing analytics, what were some of the key challenges you faced? Comments would be appreciated.