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Continuous Business Improvement Depends on Data Governance

In my last post, I explained why organizations need to consider data as an asset rather than a cost center. When we deem something to be valuable, we then need to determine how and when we’ll use it as well as secure it. We do this by establishing standards, policies and processes to define how this asset will be utilized and protected.

Let’s look at the example of an office building. Furniture and equipment are inventoried and tracked. Employees are trained on safety and security, with some developing expertise in the use of specialized equipment. Office managers know which conference rooms and desks are available for use and their locations.

Keeping this office building clean, secure, comfortable and well organized adds to the productivity of its occupants.

Without such office governance, this office building could become unsafe, unsecure, unproductive and underutilized. Do you see the parallel between this office asset example and your data? Transforming data into an asset also relies on effective data governance.

Starting a Continuous Improvement Journey

Continuous improvement

Successful data-driven companies embrace and implement continuous improvement activities to enhance results, providing a structured approach for business improvement projects. Steps include problem identification, data collection, root-cause analysis, planning process changes, implementing the changes and monitoring the results. This cycle is known as the Plan-Do-Check-Act cycle of continuous improvement, or PDCA.

Organizations committed to a continuous improvement culture, based on the PDCA cycle, depend heavily on data at every step.  Business problems can be defined in terms of waste, delays and re-work. These problems need to be quantified with actual measurements to help analysis teams detect and prioritize the next set of improvement activities.

After improvement activities have been completed, it’s important to monitor the results through feedback. It provides evidence of success, and it also helps improvement teams learn about the processes on which to focus.

Data collected about improvement processes will show symptoms of inefficiencies and waste. The analysis team then carries out root-cause analysis to determine the “levers” that can be adjusted to reduce them.  Assumptions and hypotheses will be tested and validated to find the real forces at play so the appropriate management and operational levers can be adjusted accordingly.

Scaling and Sustaining the Improvement Cycle

Companies that implement a PDCA cycle of continuous improvement realize there will be challenges in scaling and sustaining the program across multiple business areas over time.

Data collection can be tedious, especially if the associated data management activities require significant manual activity. It is common that the data available from operating databases has many problems related to quality, security, confidence, accessibility and overall understanding. These are all roadblocks the will delay the improvement activities.

If data isn’t readily available, accessible, trusted or understandable, the analysis and improvement teams can’t do their jobs effectively. This will lead to a slowdown in momentum or cause companies to abandon the improvement approach altogether.  The necessary data to drive the improvement cycle must be in an “asset class” form to sustain the improvement cycle.

Scaling the PDCA cycle involves multiple teams working in different business areas to broaden the reach of the improvement activities. Processes for finance, human resources, operations, sales, supply chain, customer service and IT may all be under analysis and evaluation.

The path to operational excellence is based on the ability to scale and sustain continuous improvement.

How Data Governance Supports the Improvement Cycle

Consider a utility company that operates a physical network delivering energy to customers. The executive team wants to reduce the time it takes for newly constructed assets to go online and reap the financial benefits of commissioning them for service more quickly.

The business improvement team starts gathering performance data from previous construction projects to determine potential areas of improvement.

They soon realize a new work management system was implemented, and the conversion of historical construction data was deemed as “non-critical” to keep the project on schedule and in budget.

The implementation team didn’t view the historical construction data as valuable from an operational perspective, so they archived it rather than covert it to the new system. This decision was made within the context of a “local” project without considering the larger analytics needs of the company.

Unfortunately, data governance was not understood or in place at this utility. If it were, the historical construction data would have been cleansed and converted as part of the new work management system’s deployment. This company failed to recognize this data as an asset with downstream analytics applications.

In this example, the decision not to convert historical data was based on managing cost at the project level. A data investment was not considered. But well-governed data is a true asset. Quality, accessibility, timeliness and understandability are fundamental to the productivity and sustainability of continuous improvement processes.

If your company is implementing any form of program to improve results, such as specialized management systems, balanced scorecards, lean management concepts, Six-Sigma or total quality management, data governance sits is at the core of long-term, sustainable success.

Improvement programs require motivation, energy and commitment at all levels of the organization. To maintain momentum, governed data assets are the key enabler, making it easier and faster to detect and diagnose problems, improve processes and validate results. There’s a direct link between the quality of improvement programs and the data assets that power them.

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Data Governance: Your Engine for Driving Results

In my previous post, I described how organizational success depends on certain building blocks that work in alignment with common business objectives. These building blocks include business activities, data and analytics.

Governance is also one of the required building blocks because it provides cohesion in the standards to align people, processes, data and technology for successful and sustainable results. Although it has been somewhat of an abstract concept, data governance is foundational to helping organizations use data as a corporate asset.

Assets are acquired and used to help organizations execute their business models. Principles of asset management require that assets be cataloged, inventoried, protected and accessible to authorized people with the skills and experience to optimize them.

Assets typically generate more value if they have high levels of utilization. In the context of data, this means governed data assets will be more valuable if they strengthen existing operations and guide improvements, supported by analytics.

As organizations seek to unlock more value by implementing a wider analytics footprint across more business functions, data governance will guide their journeys.

 A New Perspective on Data

Becoming a data-driven enterprise means making decisions based on empirical evidence, not a “gut feeling.” This transformation requires a clear vision, strategy and disciplined execution. The desired business opportunity must be well thought out, understood and communicated to others – from the C suite to the front lines.

Organizations that want to succeed in the digital age understand that their cultures and therefore their decision-making processes must become more proactive and collaborative. Of course, data is at the core of business performance and continuous improvement.

In this modern era of Big Data, non-traditional data sets generated externally are being blended with traditional data generated internally. As such, a key element of data-driven success involves changing the long-held perspective of data as a cost center, with few if any investments made to unlock its value to the organization.

Being data-driven, based on analytics, changes this mindset. Business leaders are indeed starting to realize that making data more accessible and useful throughout the organization contributes to the results they want to achieve – and must report to their boards.

If traditional asset management concepts are applied to data, then objectives for security, quality, cataloging, definition, confidence, authorization and accessibility can be defined and achieved. These areas then become the performance criteria of the new data asset class.

So transforming an organization’s leadership and the rest of its culture to perceive and treat data as an asset changes its classification from “cost” to “investment.” Valuable assets earn a financial return and fuel productivity. They also can be re-invested or re-purposed.

Data governance is key to this new perspective of data as an asset.

Data Governance Definition and Purpose

Data governance is important to the modern economy because it enables the transformation of data into valuable assets to improve top- and bottom-line performance. Well-governed data is accessible, useful and relevant across a range of business improvement use cases.

But in the early stages of implementing data governance, organizations tend to have trouble defining it and organizing it, including determining which tasks are involved.

At its core, data governance is a cross-functional program that develops, implements, monitors and enforces policies that improve the performance of select data assets.

Implementing data governance ensures that “asset-grade” data is available to support decision-making, based on advanced analytics. Using this rationale, potential objectives to meet the strategic intent of the organization can be defined to derive value.

Following is a list of possible objectives for a data governance program:

  • Improve data security
  • Increase data quality
  • Make data more accessible to more stakeholders
  • Increase data understanding
  • Raise the confidence of data consumers
  • Increase data literacy and determine the data-driven maturity level of the organization

Building a Data Governance Foundation

The scope and structure of a data governance program are important to determine and include responsibilities, accountabilities, decision rights and authority levels, in addition to how the program fits into the existing corporate structure in terms of virtual or physical teams.

Structural options include top-down command and control and bottom up collaborative networks. Executive accountability also should be outlined.

It’s common for a data executive, such as the chief data officer, to be identified as accountable for overall data governance results. Data owners are business leaders who manage the processes that generate critical data. They’re responsible for defining the polices that support the program’s objectives.

Data stewards report to the data owners and are responsible for translating data policies into actions assigned to data specialists. The data specialists execute projects and other workflows to ensure that the governed data conforms to the intent of the policies.

Data stewards form the backbone of a data governance initiative. They influence how data is managed by assigning tasks to the specialists. Data stewards are responsible for cataloging, defining and describing the governed data assets.

These roles may be full-time or part-time, depending on the scope of the work.

Key processes carried out by the data governance team include:

  1. Defining and planning the program’s scope
  2. Data quality improvement
  3. Data security improvement
  4. Metadata creation and management
  5. Evaluating the suitability of new data sources
  6. Monitoring and enforcing compliance to data policies
  7. Researching new data sources
  8. Training to improve data literacy of staff at all levels
  9. Facilitating and finding new data-driven opportunities to improve the business
  10. Leading and managing cultural change

Data governance is based on a strategy that defines how data assets should look and perform, including levels of quality, security, integration, accessibility, etc. The design and implementation of a data governance program should start with a limited scope and then gradually ramp up to support the overall business strategy. So think big, but start small.

The next post in the series explores how data governance helps implement sustainable business processes that produce measurable results over time. Click here to continue reading on.

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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