09/05/2014 | Data and Analytics
Enterprises are generating data at a growth rate of 40 percent annually. As the capacity to manage that data grows, so does the potential for better business. In fact, it could be argued that data is today’s most critical corporate asset. Many companies don’t know if their data is being properly captured, whether it is—or can be—integrated across lines of business, or if it is accurate. This creates uncertainty around how to make data actionable and fear of the consequences if the data turns out to be unreliable.
Poor data quality can lead to a potentially disastrous impact on a company’s bottom line. Technology research firm Ovum noted that poor data quality was costing US businesses more than $700 billion a year in lost customers, sales and revenue.
To help alleviate this problem, some businesses are adopting Master Data Management (MDM) solutions, hoping that technology will solve their data quality issues. The intent of MDM is to provide a single, authoritative point of reference to ensure that all information in company systems is consistently managed across the enterprise. However, MDM does not ensure data quality without human guidance and mature business processes. As a result, companies are often disappointed when MDM solutions fail to deliver the expected ROI.
In the data economy we live in today, reliable data is critical to business success. Data belongs to the business and is ultimately affected by business processes that start at the top. If an organization wants to firm up data quality, it must first establish effective data governance.
What is data governance?
Data governance is a term with an evolving definition, but what it is not is a collection of ad-hoc data correction projects. Data governance is the overarching policies and processes that govern the management of company information. The purpose of data governance is to identify what information is important, establish the processes to manage it and measure the effectiveness of the effort in achieving business objectives.
Data governance is achieved through the establishment of a focused team composed of IT and business associates. This team oversees data by documenting policies and controlling how pieces of information are captured, defined and stored in any computerized system. Four areas this team should focus on include:
1) Getting executive leadership involved and onboard with a data strategy,
2) Clear establishment of data ownership,
3) Proper documentation of standards and policies and
4) Measurement and auditing procedures.
Involving Executive Leadership in the Data Strategy
Data governance starts at the top. This is because executive leadership frequently makes decisions that affect information throughout the organization. Although senior or mid-level analysts who work daily with data may be the people who most want to drive data quality initiatives, their efforts will only succeed if the executive leadership team is directly involved. However, it is common for data quality projects to become an executive-level imperative only when the company needs to comply with regulations or is involved in a merger or acquisition. This type of initiative is often reduced to a one-off data integration or correction project assigned to IT to implement and maintain.
To motivate executive understanding and involvement in building a data-driven culture, it is necessary to start with a business case. Leadership must see the strategic benefit to doing business through data-driven initiatives, such as how reliable data can inform sales and marketing campaigns or how data can be used to ensure operational processes are having their desired impacts on costs.
When leadership is on board with the value of these initiatives, a strategy and roadmap must then be created that shows how the business will transform to the new standard over time. These efforts should not be seen as a single project, but a gradual increase in maturity that is achieved during each initiative. The chart below illustrates the maturation process for a data management maturity program, moving from little or no policies and compliance in place to automated and optimized processes that provide solid data reliability. For each organization, the value of data governance grows in tandem with the program’s maturity.
Assigning Data Ownership
Once a roadmap has been adopted, one of the first tasks for an organization’s data governance team is to clearly establish data ownership. This responsibility usually falls to data stewards.
Data stewards are responsible for documenting procedures and guidelines on how data is defined, transformed, accessed and used. If low data quality leads to poor business decisions, data stewards are held accountable. For example, if you lose revenue by sending the wrong type of customer discount offers, or if you can’t deliver your product because of problems with inventory data, there should be someone responsible.
Centralizing data management to specified individuals in this way creates impetus for quality, removes silos, and limits redundancy. These do not have to be new hires, but recognition that certain individuals in the organization need to become the authoritative resource on a specific subset of data. Most of these individuals already work with this data day to day, so assigning them ownership is simply recognizing their expertise and their accountability. Once identified, data stewards must be trained to ensure the consistency and quality of specific subsets of data.
The best people to become data stewards are usually not in IT—as one might assume—but members of the team defining business processes within the organization that actually use the data and understand its function. Data stewards must also work collaboratively with IT and particularly data custodians,the architects and database administrators responsible for the technical environment that manages how data is safely stored and transported.
Documenting Standards and Policies
It is the responsibility of the data governance team to define the standards and polices that the rest of the organization follow.
Ideally, an organization establishes data governance before attempting to implement complex data initiatives like MDM and does so to satisfy a specific business strategy. In reality, the situation is often reversed, resulting in some overlap of effort or missing data standards that were immaterial to the specific challenge MDM was implemented to solve. Fortunately, MDM is really an extension of data governance; once established, data governance will be preserved through MDM.
An essential piece of the puzzle is the definition, allowed values, and restrictions of each data element. An example of a data element is the postal address of a customer. This piece of data can be named, defined, and inputted in multiple ways, so an authoritative standard must be set to maintain consistency. Without consistency, data cannot be integrated throughout the organization, which can result in issues such as multiple departments within the company soliciting the same customer without realizing it. Ideally, each data element is assigned a data steward who documents the standards and ensures its quality.
Data stewards also examine how data enters the system through all the possible entry points. For example, data might originate through a point-of-sale dashboard, a website form, a mailed-in form, a call center, or other channels. A full data workflow must be created, mapping all of the channels and entry points where errors or inconsistencies might occur. Any transformations the data goes through also need to be documented.
As data can be defined and captured in numerous ways, with different choices benefiting different departments, the standard and policies made will result in a tradeoff—the overall organization receives consistency at the cost of what individual lines of business may be used to.
Measurement and Auditing
One of the most important aspects of data governance is measurement. Data quality must be checked against clearly defined and measurable metrics for the business to assess the result of its data governance effort. At a high level, there are two categories for metrics: quantitative and qualitative.
Quantitative Metrics: Quantitative metrics are direct measurements that assess the data itself. Some examples of quantitative metrics include completeness, validity, and accuracy:
- Completeness is the presence of a data value within a field, such as a postal address that is not missing street names, according to the rules established for that data element.
- Validity references whether the value is correct within a limited context of reference, such as whether it matches a value in a master database.
- Accuracy refers to whether or not data is correct in a real-world context, such as whether the postal address entered is a real address where mail can be delivered—according to, for example, the post office’s database.
These metrics constitute hard numbers that assess data quality. For example, a data steward might discover that postal address data is 99% complete, 87% valid and 66% accurate.
Qualitative Metrics: Qualitative metrics are more indirect. These metrics are established by the business to measure soft objectives like improved customer satisfaction, customer loyalty, business opportunity, regulations compliance, or team collaboration. For example, data collected from surveys, social media reviews, or comment cards can be used to measure customer satisfaction, but the way the business chooses to evaluate this data is subjective.
It is important all metrics are relevant to the business objectives and establish what success looks like. For some data, this may be a hard number or percentage that the business is trying to hit. For other data, there may be levels of progress or maturity that are measured according to stated objectives and supported by other metrics.
Auditing: Once measurements for data quality are established, regular audits must be done to ensure compliance. When data quality issues are identified, the source causing the quality issue must be discovered and fixed. For example, a company might discover many misspelled customer names in its database. Rather than just fixing the spellings, the data steward should investigate how the names were entered incorrectly. He or she might determine that most of the misspellings originated in the call center where intake operators guessed how to spell names heard over the phone. This could be corrected by implementing a process where call center reps are required to ask the customer for a unique identifier, such as an account number or social security number, so that the system can pull the correct customer record.
Conclusion
We are living in a data economy. The enterprise will continue to collect tremendous amounts of data regardless of whether that data is effectively managed. It is essential for businesses to understand that data quality initiatives are not done for the sake of data but for the benefit of the business. At AIM Consulting, we provide both the strategy and resources needed to help businesses establish data governance and optimize management of enterprise data throughout the entire data lifecycle, leading to more reliable business decisions and enhanced productivity and efficiency.
Building a data-driven culture requires a paradigm shift that prioritizes consistency and accountability through data governance. The result should be data that is trustworthy and available to the entire organization. When businesses come to realize the importance of owning and caring for their data they will cease to fear their data and start profiting from it.
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