Improving the Credibility of Self-Service Analytics with Data Quality

In this age of analytics, intelligence is powered by algorithms and decisions are based on facts analyzed from datasets. And Self-Service BI systems can assist businesses in making these data-driven decisions by analyzing the significant datasets without the real need of including IT professionals to produce reports or explain the facts.

Infact, with this data democratization, the complete data world has opened up for the business users for timely and logical decision making. But the question to ponder is how good this data is?

The fact is that the real value of self-service analytics has been undermined due to poor data quality. ETL and data collection techniques of the past have been hit by raw, unstructured, crowd-sourced fast-paced data inflow. And as per Experian Data Quality Survey, on average, businesses believe that 27 % of their data is inaccurate. This compels the business leaders to make decisions based on this unreliable, conflicting and siloed data, which in turn, leads to business losses.

To overcome this problem, data has to be checked for accuracy, audited, and prepared for use by self-service analysts, and organizations are increasingly understanding this. As per Gartner, the data preparation market will reach $1billion by 2019, with 30% of organizations adopting some form of self-service data preparation.

Let us try to find ways in which organizations can overcome these data challenges and ensure data quality required for the business users.

Getting the basics right

Begin with correct data entry as this is still a big issue and needs to be taken care of. Data entry and system errors need be resolved at the origin rather than in the ETL processes later, making it a problematic task.

Moreover, parameters for deciphering and standardizing data values needs to be discussed and consented upon within the organization.

Laying down clear guidelines

Companies should create a set of data management rules, policies, and procedures to eliminate errors, duplicate entries, and inconsistencies in their dataset to ensure Enterprise Data Quality,

Likewise, accuracy expectations must be clearly specified for high priority datasets, and companies should ensure that these datasets must be validated for their compliance against these expectations.

Moreover, assigning a data quality role or executive sponsorship to oversee the entire data management process can make a huge difference.

Executive Sponsorship

The role of the executive sponsor can be helpful in establishing a good governance structure by ensuring the right technological environment, defining operational resources and procedures, and in creating a resource plan.

To be precise, executive sponsorship ensures sophisticated data quality management so that data change can be handled aptly for the users. Data Stewards must also be designated to preserve data integrity.

Data Change Management for Usability

Non-technical users, when using these datasets, will be required to integrate and enhance data from different data sources, and further, in order to relate these sources, they will be required to introduce changes, resulting in multiple copies of data.

To tackle this issue, good self-service BI governance, systems, and data quality solutions must be in place enabling companies to organize the organizational dataset into distinct and logical data groups based on specific criterion still keeping it all in a single place for a 360-degree view of the business operations. Then users can be added and managed accordingly.

Data quality solutions can further assist in maintaining a logical storehouse of personalized data changes, meaning and context, which otherwise, could result in data chaos.

Hence, shifting from traditional models to more fluid self-service approach can be best facilitated via a robust data management system offering useful controls to ease the transition.

Master Data Management (MDM) System and other data quality solutions

MDM system can be helpful in creating a single source of truth for business processes through a blend of technology solutions including data integration, data quality , and business process management. Users can access any dataset from a single place and analyze and drill-down into.

Besides MDM, there are also a number of data preparation platforms/tools that transforms data for business users to analyse. Data cataloging assists in providing a searchable repository of metadata for improved data management, and there are other self-service data preparation offerings.

To conclude, by consistently standardizing, transforming and maintaining clean source data, along with managing the data quality through the application of robust data management system under the guidance of assigned data quality professionals can help streamline the self-service data preparation process ensuring better business decisions and early product releases.

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