Does your organization need a data management strategy?

When asking about your organization’s data strategy, it’s important to ask the right questions. Try some of these: Is your data telling you a story? Does it tell the right story?

Amazon’s retail presence has grown over several decades by asking, “What will our customer buy next?” The company uses purchase history, spending habits, insights such as feedback and demographics and a dash of business intelligence to predict customer behavioral trends and augment sales. 

Imagine this scenario: A consumer buys the first two books of an unfinished trilogy from Amazon.com. When the author publishes and releases the third and final book, Amazon sends them a direct email highlighting the book release and provides a link to go directly to their site for purchase. These days that seems like a common event, but in the early days of e-commerce, the idea that a retailer would keep close track of consumers’ individual spending habits was revolutionary. And, critically, it led to massive growth for the retailer.

Famously, through forward-looking analytics, Target figured out a customer was pregnant and began mailing her house coupons for baby clothes. The only problem? She was a teenager, and she hadn’t informed her outraged father of the news. Target’s annual revenue numbers have jumped from $44 billion in 2002 to $67 billion in 2010 to $107.4 billion in 2023. The retailer’s growth can be partially attributed to these data-driven marketing campaigns.

There are business intelligence examples in the nonprofit world as well. After only the first two weeks of a new semester, a community college in Indiana can predict with 70% accuracy whether or not a student will fail a class. All of this comes from collecting and analyzing 14 days’ worth of virtual attendance data from their learning management system.

The college’s ability to take action — reaching out to students before the end of the semester and helping them resolve educational issues — has allowed the school to achieve historic drops in poor grades and an increase in student retention.

How do these organizations get to the point of powerful actions based on data they already possess? It’s not as easy as flipping a switch. Data-driven decision-making takes years of progress and arrangement to build. At the core, these organization-changing actions are uncovered by 1) asking the right questions and 2) having confidence in clear, accurate data. 

Data is comparable to a bucket full of Legos. Until you create something, the bucket is just a collection of bricks you keep in storage. And to construct something specific, you often want the instruction manual which comes in the form of a data management strategy. Building your data management strategy involves three key pillars: policy, compliance and quality.

Policy

When people hear the word “policy,” they often think of red tape or undergoing some painful process —jumping hurdles or circumventing roadblocks — in order to complete a task. Data management policies are quite the opposite. When created with business impact in mind, policies are positive guidelines that help the organization reach goals. They are also the cornerstone of data governance. 

Picture a highway where traffic is free-flowing effectively because everyone knows the rules of the road. This is what data governance enables for information sharing within an organization. When there is a lack of standards in place, you’ll start to see traffic jams, bottlenecks, useless on-ramps and vehicles that belong on a different highway altogether. Effective policies outline responsibilities, categorization of data and expectations on application integrations among software platforms.

Compliance

Many organizations are governed by federal data regulations, such as HIPAA, HITRUST, PII or PCI. Federally funded nonprofit organizations have strict obligations to report on organizational effectiveness. In these cases, industry governing bodies dictate requirements that must be incorporated into a data management strategy. Data access may need to be restricted to a designated group. Information may need to be retained for a certain number of years (or disposed of after a specified amount of time). Reports may need to be submitted on a monthly, quarterly or annual schedule. These responsibilities, along with associated privacy and security measures, are a critical aspect of data management.

Quality

Does your data provide enough clarity so you can take confident action? Does it tell a story that leads to a business decision? There needs to be trust in data before there is trust in reported information. Only then can there be confidence in a plan of action.

The higher the quality of your data, the easier this process becomes. High-quality data is easy to interpret, more applicable to your business goals, easily shared throughout the organization, and accurate and reliable. A management strategy includes processes to identify and repair or enhance poor-quality data.

Without guidelines, poor data quality will hinder an organization’s decision-making capabilities. If marketing and sales teams don’t have an agreed-upon metric for realizing revenue, two reports run on the same data may tell completely different stories and could mistake a good month for a bad month. Nonexistent guardrails lead to inconsistent reporting.

Step one in improving data quality is to create a data catalog. This is an enterprise-wide inventory of all the organization’s data. It includes technical information (what tables, columns and other structured feature sets build up the data?), business information (what is the data used for, and where did it originate?) and operational information (who is responsible for the data, who last used or changed this data and when did we last back up our data?). A data catalog communicates where to find and how to understand data collected by the organization.

High-quality data is more valuable than untouched, unconditioned data. Why do companies like Facebook and Google spend time standardizing and cleansing collected customer data? Because clear, concise information on consumer activity is worth a pretty penny on the open market. Ethical or not, these companies could predict (with a certain degree of accuracy) the purchasing habits of a 65-year-old grandfather of two on the other side of the world during Mondays of the winter season. Their ability to use predictive analytics in this fashion is because of the investment made to increase the quality of their (massive amounts of) collected data.

Data stewards

Data policy, compliance and quality comprise the framework of data management. Data stewards carry out the plan. These are appointed individuals accountable for overseeing the organization’s data strategy. They are responsible for knowing the value of data in a business setting and understanding the system architecture in which data is stored and consumed. 

A data steward can be an employee in a technical position, in a strategic position, or both. They could be in leadership; they could be an individual contributor on your data team, or they could be a cross-functional player working with multiple departments throughout the organization.

Data stewards work to keep company data organized by following quality standards. For example, an organization may have many different applications that store date values. Dates could be stored using either two or four digits for the year. They could contain dashes or slashes or spaces. They could omit the day and only record the month and the year … the list goes on. 

It’s difficult to write reports that query dates when there are so many possible varieties. A data steward ensures these separate data points all land inside the same format when stored or shared, depending on which version is optimal for the organization overall. The steward is the resource who knows how the various sales, marketing, delivery or leadership teams need to read and consume information.

It would be rare for one person within an organization to have expertise on every data source and business function. More commonly, a team of data stewards is needed to fulfill a management strategy. 

Multiple data stewards form a data governance council, which is a group that meets regularly — monthly, quarterly or annually — to share information and updates around data management. Council membership should be assembled from multiple areas of the organization, not just a single silo, in order to capture multiple company needs and viewpoints. Data owners, such as a chief information officer (CIO), chief data officer (CDO) or founder, are also included as members. 

A council meeting has several purposes. Issues (such as our date problem above) are discussed and resolutions are agreed upon and documented. Data policies are created, defined and continually refined. Quality standards are adjusted to create more business value or to fulfill new industry requirements. Data inputs from external sources are modified to create a cleaner reporting dataset. And compliance standards are monitored, upheld and verified.

An organization’s focus on a data management strategy leads to new and exciting pathways, including the usage of business intelligence, machine learning and artificial intelligence. Results and breakthroughs from data-mature organizations are told in stories across all industries.

• In Latin America, Microsoft and Wipfli teamed up to create an anti-corruption tool that uncovers collusion between government employees and dishonest contractors. The solution uses machine learning against a data set of common pricing models for government infrastructure contracts.

   

• Industrywide, cybersecurity providers have adopted artificial intelligence processes to predict and thwart threats and attacks on customer organizations. These innovative methods reduce risk by staying one step ahead of emerging security events.

   

• Healthcare organizations are providing convenient and meaningful patient experiences by offering remote monitoring options and optimizing data-assisted clinical care decisions.

Over time, organizations that adopt a data management strategy will secure a strategic advantage over competitors due to their ability to drive innovation, plan and act quickly, improve customer experience and support organic growth.

How Wipfli can help

If your organization is ready to get serious about data management, our dedicated professionals can help set you up for success. We have the extensive industry experience you need to build a data management strategy that works for you, with the tools you need to turn your critical information into actionable insights. Contact us today to get started.