4 min read
Top 5 Obstacles 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 need to be arranged first. Below you'll find the top 5 obstacles for analytics environments. Make sure you address these 5 obstacles within the organization so the new platform can function optimally and bring the business a step closer to operational efficiency.
1 Ad Hoc reporting in Excel
When there's no mature analytics environment, or it hasn't been properly adopted by the business, you notice that exports are made from CRM and ERP systems. These exports are then opened in Excel to meet ad hoc reporting needs. This causes an overgrowth of Excel files, making it unclear what the data quality is and who's working with the most recent data. Before switching directly to an analytics environment, an inventory of Excel users should take place. This 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 with governed data can create their own dashboards and reports.
2 Data silos
Marketing and Sales often use the same data but sometimes get it from different sources. This is because different departments often don't have access to each other's data. This phenomenon is called 'data silos'. These are essentially closed-off data sources that only certain people or a single department have access to. By processing the business's data needs into a central database such as a data warehouse, you ensure that the entire business has safe and governed access to the data it needs.
3 Business – IT Alignment
In the absence of access to the necessary data, 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 the reports once the data request is no longer up to date. In addition, IT often produces 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's often only noticed later that different KPI definitions exist within the business. By figuring this out beforehand, you prevent many problems with the adoption of the chosen business intelligence software. It's also important to agree on how detailed the KPIs should be. Management often wants an overall overview with KPIs, while at the operational level there's 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's important that data is safeguarded in a secure and central location. A data warehouse is an example of such a location. Besides a central data warehouse, it's also important to arrange version control and data lineage. When data lineage is arranged, you know exactly where data is pulled from and which files depend on which data sources. Version control ensures that changes in analytics applications are documented and stored. This way, you can restore an older version in case of incorrect changes.
Prevent obstacles
These 5 obstacles can easily be prevented with a clear plan of approach. E-mergo can develop a special roadmap based on these and other points of attention. This way, we ensure you can make 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.