Click to learn more about authors Tejasvi Addagada and Gopi Maren.

Every day organizations rely on reports to understand information in their day-to-day operational and strategic decisions. For all the dashboards or score-cards, today’s data is sourced from distributed and disparate sources of data.

When we hear from someone in the organization that “My reports are not making sense”, the report owner usually reaches out to report developers for corrections in the report.

Who should be held responsible for the reporting requirements?

Is it the report developer or Business Analyst or Data Analyst from System(s)?

This gap is common and evident across the organizations. Let us look at challenges in Business Intelligence that organizations are seeing in 2018:

• Businesses often lack the right data and analytics organizational structure. Every department hosts its own data that is not interoperable.
• Organizations do not have the right resources, skills or technologies to enable the changes that a future-oriented strategy can bring.
• Lack of valid, current and consistent data in the data sources.
• Low level of understanding of certified provisioning points.
• Availability of abundance of data but lack of culture and human capabilities to properly collect, analyse and manage data
• There are myriad of tools for Business Intelligence, but the basic capabilities of the tools are not being leveraged due to bad quality data
• Data Quality Issues identified during the rules management in BI platforms are not sent back to systems of records for cleansing.

Tackling the Business Intelligence

Good Quality Data Sourcing

The reports are always right when we have good quality data in the systems of records. Rather than spending on additional capabilities offered by vendors, the advice is to actively manage the data issues in the systems and processes; and thereby leveraging the same for the reporting requirements.

Managing Metadata

It is a good practice to capture definitions of the data elements while also understanding if these elements are used as a reporting attributes in compliance, risk reporting. Capturing this information will be assisting the analysts in better understanding the context in which an element is being created in, along with the context it is being used in. Further, knowledge of the reporting attributes for Compliance reports can better assist the data owner focus on these datasets.

Formalizing Data Quality Management

This is where, Data Governance can assist in formalizing a Data Office function across the enterprise. Data Governance is an oversight on Data Management activities to ensure that policy and ownership of data is enforced in the organization.  The emphasis is on formalizing the Data Management function along with the associated Data Ownership roles and responsibilities. In addition, governance also ensures that Data Management as a service is sustainable as a function there by enabling active quality management of data.

Leveraging sunk costs associated with tools

Tools need not be at fault, most often, but the problem as we by now know lies with the underlying data. There are additional capabilities in the forms on other tools or APIs that can provide quality of data being used for reporting. There are further capabilities like the ability to fix issues or raise issues through workflow.

Data collection and Data cleansing involves personnel working on these aspects, and it’s the responsibility of the business to fix the Data Quality and also to enjoy the outcomes from having to develop insights.