Best Practices

For the very beginning, we should be aware that the most powerful analytical tools are only as good as the data they crunch.

This means that intelligence built on bad data can be worse than no analysis at all. That's maybe surprising, but it's a fact.

It's not rare case that lots of companies make important decisions or formulate strategic objectives with full knowledge of facts that their data is flawed or incomplete.

Companies have spent many years building enterprise data warehouses and using business intelligence (BI) tools to report on the business. But predictive analytics is different. Advanced statistical, data mining, and machine learning algorithms dig deeper to find patterns that traditional BI tools may not reveal.

When you need to select a business intelligence product, it’s important to consider lots of factors.
At first place, specific product features like ease of use, ease of implementation and administration,
scalability, user-interface options, and how well it integrates into your company’s
existing and future platform environment.

Among the most important of these considerations are:

In order to make success in analytics, both strong analytical leaders and executives who are willing to make a long-term commitment are needed. Nowadays, the analytical leaders spend as much time discussing how to manage people, projects, and processes as they do technology and architectures.

In our last article, we talked about basics of KPI, what are they, and why do you need them.
Now, we are going to go through defining the KPIs in the right way, how to track and analyse them.

This article covers basics on KPIs, what are they and why we need them.

Let's first define what is KPI?

Shortly, KPI's or Key Performance Indicators are a set of performance statistics specific to your business.
They offer an overview about how you are performing against goals of your company.