5 Ways to Use Data Analytics

The Business Intelligence (BI) market is growing fast – at a Compounded Annual Growth Rate (CAGR) of 12% between 2019 and 2024 (Research and Markets). Even in this pandemic, the need for data analytics continues to grow as 29% of respondents were seen working on predictive analytics projects and 22% on descriptive analytics projects. Clearly, businesses have captured the big picture – customer experience is directly connected to data and insights. The adoption of BI and data analytics is going to get even stronger ahead.

Developing your own approach to data analytics

But ask any successful, happy user of data analytics, and you will often find that the secret to the solution’s success lies in the organization’s own approach. This is determined by what considerations a user will apply for tapping insights out of data. Whether you are developing something specific for your enterprise or picking an integrated stack, you can also master some key areas that will amplify your data analytics strengths.

1. Architecture A lot of the troubles that surface with data analytics can be attributed to how meta-data is defined, designed, and aligned in the system. Ensure that the tool you use has got the right meta-data labels and that it reduces the underlying complexity and inter-relationships for users. This can be done by taking into account various areas like reporting, authoring, data entities, dashboards, and analysis flows.

2. Intuitiveness and Simplicity It is easy to get caught up in the scale and functionalities of a tool. That is when the basic aspect of user-friendliness is ignored. But this should not be an after-thought. Reports and data collection methods should always be designed with the end-user in mind. If a business user is unable to tap this tool easily and in real-time, the user will either complicate the process or lean towards old methods. These users might even resort to shadow IT. So focus strongly on engagement levels of a tool.

3. Training Even the best tools need some form of user skills before they can start delivering the expected outcomes. Competencies should be sharpened without any delay. Self-service tools and friction less solutions make this process simple for both the enterprise and the user.

4. Features Not all the features listed in a tool’s brochure may be relevant for you. So sit down and design a framework together with the solution team. The idea should be to enhance the tool for the kind of querying, indexing, integration, semantic layers, data sources, authoring, and execution time that your enterprise may specifically need to focus on.

5. Common pitfall-caution Many tools fail to deliver the outcomes they promise because they end up perpetuating a host of problems. These problems can be easily averted with long-term mindsets, careful planning, and regular monitoring. So try to avoid duplication, unnecessary documentation, compliance gaps, data availability issues, data governance problems, and lack of integration layers. It is also important to undertake adequate quality assurance audits, configuration tests, and warehousing checks at the right time.

Above all, it helps if you define your business problem and use-cases well before mindlessly deploying any tool that looks good on paper. Solutions come alive when they are applied to address well-defined problems. Once you have clarity on what you want the tool to tell you, and what insights your users would gain from, it is easy for you to get tangible and intangible ROI (Return on Investment) from data analytics.