Tag: data-driven analytics

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Data Governance Helps Build a Solid Foundation for Analytics

If your business is like many, it’s heavily invested in analytics. We’re living in a data-driven world. Data drives the recommendations we get from retailers, the coupons we get from grocers, and the decisions behind the products and services we’ll build and support at work.

None of the insights we draw from data are possible without analytics. We routinely slice, dice, measure and (try to) predict almost everything today because data is available to be analyzed. In theory, all this analysis should be helping the business. It should ensure we’re creating the right products and services, marketing them to the right people, and charging the right price. It should build a loyal base of customers who become brand ambassadors, amplifying existing marketing efforts to fuel more sales.

We hope all these things happen because all this analysis is expensive. It’s not just the cost of software licenses for the analytics software, but it’s also the people. Estimates for the average salary of data scientists, for example, can be upwards of $118,000 (Glassdoor) to $131,000 (Indeed). Many businesses also are exploring or already use next-generation analytics technology like predictive analytics or analytics supported by artificial intelligence or machine learning, which require even more investment.

If the underlying data your business is analyzing is bad, you’re throwing all this investment away. There’s a saying that scares everyone involved in analytics today: “Garbage in, garbage out.” When bad data is used to drive your strategic and operational decisions, your bad data suddenly becomes a huge problem for the business.

The goal, when it comes to the data you feed your analytics platforms, is what’s often referred to as the “single source of truth,” otherwise known as the data you can trust to analyze and create conclusions that drive your business forward.

“One source of truth means serving up consistent, high-quality data,” says Danny Sandwell, director of product marketing at erwin, Inc.

Despite all of the talk in the industry about data and analytics in recent years, many businesses still fail to reap the rewards of their analytics investments. In fact, Gartner reports that more than 60 percent of data and analytics projects fail. As with any software deployment, there are a number of reasons these projects don’t turn out the way they were planned. Among analytics, however, bad data can turn even a smooth deployment on the technology side into a disaster for the business.

What is bad data? It’s data that isn’t helping your business make the right decisions because it is:

  • Poor quality
  • Misunderstood
  • Incomplete
  • Misused

How Data Governance Helps Organizations Improve Their Analytics

More than one-quarter of the respondents to a November 2017 survey by erwin Inc. and UBM said analytics was one of the factors driving their data governance initiatives.

Reputation Management - What's Driving Data Governance

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin-UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organizations but don’t have a formal governance strategy.

Data-driven Analytics: How Important is Data Governance

When data governance helps your organization develop high-quality data with demonstrated value, your IT organizations can build better analytics platforms for the business. Data governance helps enable self-service, which is an important part of analytics for many businesses today because it puts the power of data and analysis into the hands of the people who use the data on a daily basis. A well-functioning data governance program creates that single version of the truth by helping IT organizations identify and present the right data to users and eliminate confusion about the source or quality of the data.

Data governance also enables a system of best practices, subject matter experts, and collaboration that are the hallmarks of today’s analytics-driven businesses.

Like analytics, many early attempts at instituting data governance failed to deliver the expected results. They were narrowly focused, and their advocates often had difficulty articulating the value of data governance to the organization, which made it difficult to secure budget. Some organizations even viewed data governance as part of data security, securing their data to the point where the people who wanted to use it had trouble getting access.

Issues of ownership also hurt early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve these issues of ownership because many are adopting a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy weaves itself into the business and its infrastructure. It is present in the enterprise architecture, the business processes, and it helps organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing because organizations and their data are constantly changing and transforming, so their approach to data governance needs to adjust as they go.

When it comes to analytics, data governance is the best way to ensure you’re using the right data to drive your strategic and operational decisions. It’s easier said than done, especially when you consider all the data that’s flowing into a modern organization and how you’re going to sort through it all to find the good, the bad, and the ugly. But once you do, you’re on the way to using analytics to draw conclusions you can trust.

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You can determine how effective your current data governance initiative is by taking erwin’s DG RediChek.

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Why Data Governance Leads to Data-Driven Success

Searching for new ways to generate value and improve execution, organizations of all shapes and sizes are racing to embrace data-driven approaches that are enabled by advances in analytics.

A perfect storm of events that started in the mid-2000s has morphed into a disruptive force in the economy at an accelerating pace. Data-driven analytics has gained mainstream business adoption. Advances in communications, geo-positioning systems, sensors and computing technologies have combined with the rise of social media and the incredible growth of available data sources.

Leadership teams in the boardroom have become acutely aware of the potential opportunities available for driving innovation and growth.

Although opportunities are significant, many challenges exist that make it difficult to successfully adopt data-driven approaches.

We’re going to explore the rationale for becoming data-driven, how to frame success, and some of the critical building blocks required, including data governance.

Framing Data-Driven Success

Organizational impact helps us frame the concept of data-driven success. Impact is related to an outcome. An impact describes a changed condition in measurable terms. A well-defined impact is a proxy for value.

Stating that you want to “move the needle,” implies that the area of impact can be measured with a metric that represents that needle. By achieving impact in the right business area, incremental value is created.

When investments are considered for implementing new data-driven approaches, it’s essential to define the desired areas of impact. Evidence of impact requires knowledge of the condition before and after the data-driven approach has been implemented.

Areas of impact can be tangible or intangible. They might be difficult to measure, but measurement strategies can be developed that measure most areas of impact. It’s important to frame the desired area of impact against the feasibility of gathering useful measurements.

Examples of measurable impact:

  • Increase process efficiency by 5%
  • Reduce product defects by 15%
  • Increase profit margin by 10%
  • Reduce customer attrition by 15%
  • Increase customer loyalty by 20%

Impact measures relative changes in performance over time. The changes are directly related to incremental value creation. Impact can be defined and managed by organizations from all sectors of the economy. Areas of impact are linked to their mission, vision and definition of success.

Data-driven excellence describes the performance that exists when targeted areas of impact are successfully enabled by data-driven approaches.

Building Blocks of Data-Driven Approaches

Successfully becoming data-driven requires that desired impacts are related to and supported by four categories of building blocks.

Data-Driven Building Blocks

The first category describes the business activities that must be created or modified to drive the desired impact. These are called the “business building blocks.”

The second category describes the new information and insights required by the business building blocks based on analytic methods that enable smarter business activities. These are called the “analytics building blocks.”

The third category describes the relevant data to be acquired and delivered to the analytics methods that generate the new information and insights. These are called the “data building blocks.”

Success at an organizational level requires that all critical building blocks are aligned with shared objectives and approaches that ensure cohesion and policy compliance. This responsibility is provided by the fourth category called the “governance building blocks.”

The four categories form a layered model that describes their dependencies. Value-creating impact depends on business activities, which depends on analytics, which depends on data, which depends on governance.

The Governance Imperative

Data-driven approaches touch many areas of the organization. Key touch points are located where:

  • Data is acquired and managed
  • Insights are created and consumed
  • Decision-making is enabled
  • Resulting actions are carried out
  • Results are monitored using feedback

Governance at a broad level develops the policies and standards needed across all touch points to generate value.  As a form of leadership, governance sets policies, defines objectives and assigns accountabilities across the business, analytics and data building blocks.

Business activity governance ensures that proactive management and employee teams respond to new sources of information and change their behaviors accordingly. Policies related to process standards, human skill development, compensation levels and incentives make up the scope of business activity governance.

Analytics governance ensures that all digital assets and activities that generate insights and information using analytics methods actually enable smarter business activities. Policies related to information relevance, security, visualization, data literacy, analytics model calibration and lifecycle management are key areas of focus.

Data governance is focussed on the data building blocks. Effective data governance brings together diverse groups and departments to enable the data-driven capabilities needed to achieve success. Data governance defines accountabilities, policies and responsibilities needed to ensure that data sets are managed as true corporate assets.

This implies that governed data sets are identified, described, cataloged, secured and provisioned to support all appropriate analytics and information use cases required to enable the analytics methods. Data quality and integration are also within the scope of data governance.

Foundation for Success

Companies that are successful with data-driven approaches can rapidly identify and implement new ideas and analytics use cases. This helps them compete, innovate and generate new levels of value for their stakeholders on a sustainable basis.

Data governance provides the foundation for this success. Effective data governance ensures that data is managed as a true corporate asset. This means that it can be used and re-purposed on an on-going basis to support new and existing ideas generated by the organization as it matures and broadens its data-driven capabilities.

As organizations unlock more value by creating a wider analytics footprint, data governance provides the foundation necessary to support their journey.

The next post in this blog series dives deeper into data governance in terms of scope options, organization approaches, objectives, structures and processes. It provides perspectives on how a well-designed data governance program directly supports the desired data-driven approaches that ultimately drive key areas of business impact.

Data governance is everyone's business