9 signs your data quality program is off track and 4 clues to get it back on track

As we watch the business landscape rapidly evolving - with the emergence of new business innovations and new forms of competition, catalysed by advances in digitization, analytics, artificial intelligence, machine learning, internet of things or robotics - the availability of quality data is more than ever a critical requirement, allowing to yield the full potential of these new technologies to enable the competitive edge, that makes the difference in an increasingly competitive business environment.

Data quality has always been a challenge to all organizations, but it has never been so challenging as it is now: with increasing data needs and more complex business requirements, with larger volumes of data available from the most diverse sources.?

Starting and successfully managing a data quality program, with focus on leveraging the business strategy, is an ambitious goal and often the results are far from the expected in multiple levels. These tend to be expensive initiatives, time and resource consuming and often?intrusive and disruptive, creating the natural resistance to change within the organization.

What is wrong?

For those involved in starting or running a comprehensive data quality program, there are some signs that reveal that changes need to be made to get your program back on track.

1. Your organization's data is clean and accurate.

One of the most current definitions of quality data defines it as "data that is fit for purpose," however, there is never a single purpose for a single data element and data can simultaneously have different levels of quality.?

Frequently the metrics of data quality are very tightly connected to IT and to the systems that produce and manage that data, and those are usually compliant with the requirements, so by definition the data they hold has the necessary quality.?

Except a technical requirement is not a business requirement and data must be considered from a business perspective, under business requirements and needs. And when looking from this perspective, the perception of data quality can dramatically change.

2. Your organization's data has a single business definition

A customer is a customer.?

It is not. Each area within the organization defines a customer in its own way, each system holding customer data handles this data with different rules and purposes.

This highlights the importance of the existence of a data catalogue and business glossary, enabling consistency in data usage across the organization and providing context to key stakeholders to find and understand data.?

3. Skipping the assessment phase

It is critical to have a comprehensive view of the organization, a clear understanding of the data environment. Addressing data quality cannot be reduced to handle a data set in a given database. Addressing data quality is to address the three dimensions that directly affect data: people, processes and technology. The starting point to an effective data quality initiative it is that data is assessed in this broaden perspective.

4. Not profiling and interrogating data values

Applying a solution without the full understanding of the problem does not lead to success. It is essential to understand the data and its impacting factors.

A detailed profiling of the data and subsequent analysis of the results - identifying the root causes and possible remediation actions - is essential to avoid the recurrence of the issues and the allocation of time and resources in initiatives that will hardly bring the expected outcomes.

5. Not creating and using data quality standards

A data standard is nothing but formalizing representation, format, and definition for data elements, ensuring homogeneous routines for data entry, directly leading to higher levels of quality throughout the data life cycle.

6. Not following the data quality roadmap

Data quality is a means to an end, not an end in itself.?

It is important to define clear objectives, aligned with business objectives, strongly supported in business cases, and plan accordingly. The roadmap can and should be revisited and adapted to changing circumstances, but what should be avoided at all cost is to fall in a cycle of detached initiatives that contribute very little to the organization's objectives.

7. Building the data quality program as one large project

Addressing a data quality program as one or more large projects, impairs the capability to deliver effective results in reasonable time frames,?making it hard, even with a strong sponsorship, to keep the necessary traction to complete all the necessary changes.

Starting with smaller projects - that combine a reasonable funding model, with short time frames, targeted and with the necessary internal engagement - will allow delivering targeted return on a short time frame and leverage subsequent initiatives.

8. Viewing technology as the entire solution

Exclusive focus on a technological approach will only address part of the problem.?

Data quality must be approached in a holistic perspective, simultaneously handling?people, processes and technology as a whole, creating solutions that involve these three dimensions.

9. Not continually monitoring and evaluating data?

Data quality is a continuous cycle of assessment and remediation.

Continuous monitoring will improve and sustain the quality of critical data and simultaneously emphasize the importance of data leading to better data quality.

What to do about it?

A successful data quality program may be a daunting task, although not impossible depending on the approach.

1. Start with business areas than can clearly identify and measure the business impact of bad data on their processes

In every organization the opportunities to identify these cases are abundant.

Across all the business areas there are pain points related with the quality of data and identifying them is not a challenge.

2. Build your business case with those willing to defend it

Once you have identified a critical pain point, you will have the business stakeholder that can passionately and effectively articulate the impacts of poor data quality in his processes and that will be eager to defend the project.

3. Focus on turning insights into action

Having the business stakeholder working by your side will accelerate the process of quickly move from the findings to specific actions.

4. Establish data quality targets based on critical data for business

A deep understanding of the impacts of bad quality data on the business processes enables a more accurate prioritization of the critical data, hence making easier to identify clear targets on an early stage.