Top 5 Stumbling Blocks for Analytics Environments

More and more companies see revenue models in analytics applications, but before an analytics platform is set up, a number of things must first be sorted out. Below you will find the top 5 stumbling blocks for analytics environments. Make sure you avoid these 5 stumbling blocks within the organization so that the new platform can function optimally and take the business a step closer to operational efficiency.

1 Ad hoc reporting in Excel

When there is no mature analytics environment, or it has not been adopted properly by the business, you will notice that exports are made from CRM and ERP systems. These exports are then opened in Excel to meet ad hoc reporting needs. This leads to an Excel sprawl in which it is unclear what the data quality is and who is working with the most recent data. Before switching directly to an analytics environment, an inventory of Excel users should be made. That way you know who needs their own dynamic dashboard and who only needs a static report. This helps you estimate the number of users of a (new) analytics platform, where users can create their own dashboards and reports with governed data.

2 Data silos

Marketing and Sales often use the same data but sometimes retrieve it from different sources. This is because different departments often have no access to each other's data. This phenomenon is called 'data silos'. These are essentially closed-off data sources that only a few people or a single department can access. By incorporating the business's data needs into a central database such as a data warehouse, you ensure that the entire business has secure and governed access to the data it needs.

3 Business-IT alignment

When access to the required data is lacking, problems often arise between IT and the business. In such a situation, the business submits requests to IT for reports and other data. Producing these reports is time-consuming, so the business often only receives them when the data request is no longer current. In addition, IT often builds the report without knowing exactly what the business user wants to see. This problem is often summarized with the terms 'Business-IT Alignment' or the 'Business-IT Gap'.

4 KPI definitions

During the development of an analytics platform, it is often only noticed later that different KPI definitions are in use within the business. By sorting this out in advance, you prevent many problems with the adoption of the chosen business intelligence software. It is also important to agree on how detailed the KPIs are. Management often wants an overall overview of KPIs, while at the operational level there is a need for detailed KPIs.

Marketing: “An account is a converted lead.”
Finance: “An account, for reporting purposes, is a customer with money on deposit who can trade with us.”
Accounting: “An account is a recorded entry in our back office, books, and records system.”
Legal: “An account is a structured product we offer our customers through a legal agreement that they sign.”

5 Data architecture

When organizations start implementing analytics software, it is important that data is safeguarded in a secure, central location. A data warehouse is an example of such a location. Besides a central data warehouse, it is also important to set up version control and data lineage. When data lineage is in place, you know exactly where which data is pulled from and which files depend on which data sources. Version control ensures that changes to analytics applications are documented and stored. That way, if a change turns out to be faulty, you can for example restore an older version.

Avoid stumbling blocks

These 5 stumbling blocks are easy to avoid with a clear plan of action. E-mergo can develop a dedicated roadmap based on these and other points of attention. That way we make sure you can earn money with analytics or save costs. Curious how we do this? Feel free to get in touch with us to hear more about our approach.