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What Your Data Governance Approach Can Learn from Toyota

Maximize data to fuel business processes and business results

Toyota's desire to become more disciplined, organized, and streamlined to improve overall customer value very much depended on the "Lean manufacturing" process it created in the 1950s and 1960s. This "Lean" methodology was designed to identify and eliminate waste in business processes and manufacturing to improve efficiency and promote effectiveness. Through the application of "Lean Thinking," the company has grown from a small automobile manufacturer to one of the ten largest companies in the world and an incredibly profitable one at that.

Lean methodologies have been adapted and adopted by some of the largest and most successful organizations around the world including service organizations as they strive to improve business processes. The Lean Approach is commonly applied in parts of a business where the business process is visible and known to the organization. However, there is another area that can benefit from a lean approach - Data Governance.

Data Governance is often overlooked as it performs "business as usual" functions in the background, but there is a perfect synergy between the objectives of Lean and those of Data Governance. Both disciplines have core principles focused on driving efficiencies, minimizing waste and delivering robust, reliable and timely outputs. By applying a Lean methodology to data management and governance, you can enable your business to maximize the value of data and process outputs by both eliminating and preventing wasteful activity.

Five Data Governance Disciplines
Before investigating where and how waste can be eliminated and prevented, let's first define what it means to leverage a lean methodology in data governance. A Lean Data Governance approach focuses on the creation and refinement of processes that deliver robust data and information to the internal data customer. This method enables a business to review and improve each process stream within the Data Governance program. There are five Data Governance Disciplines that can help guide the exploration of a governance program where Lean principles can be applied:

  1. Strategy
  2. People and Organization
  3. Process
  4. Tools and Technology
  5. Data Management

By evaluating waste prevention and waste elimination against these disciplines, organizations can investigate where the issues may be in their data governance program and begin to establish a lean approach.

Waste Elimination
Keeping in mind the five Data Governance disciplines, we can categorize waste into each of the following, which enables the business to implement a Lean Data Governance approach:

  1. Transportation: This refers to the waste that is involved in transporting data and information from different systems and repositories to its end destination.
  2. Inventory: Responding to customer demand ensures that the business is not holding surplus data, overproducing reports which are then archived, or holding product codes that are no longer used.
  3. Motion: This can be unnecessary movement of data or information within a process. When data is processed for quality assessments or analytical purposes, the tools and technology used to move, manipulate and interrogate that data should be evaluated.
  4. Waiting: Waiting for essential outputs such as data, reports, alterations, or something very specific to the governance process results in a negative impact across the organization as well as wasted time.
  5. Over processing: Data goes through many processes, some of which are necessary and some of which are performed out of habit. Oftentimes, when you dig in, you will find some processes are embedded in the business and no one really knows why they are done.
  6. Over production: This is the production of more than what the internal data customer demands. Whether it's producing excess data, reports, analysis or excess number of meetings, emails and documentation, overproduction leads to a wasted time and effort.
  7. Defects: Defects in data can occur regularly and repeatedly. These may go unresolved at the source as teams deal with it at a later stage, often using well established workarounds.

The elimination of waste through these seven areas allows the business to optimize data governance and the associated processes. Evaluating each Data Governance discipline within a process will help further pinpoint areas where waste is occurring, eradicate it and focus on prevention.

Waste Prevention
Waste prevention requires organizations to take a closer look at how they are streamlining governance processes and feeding into the prevention of unnecessary activity. Using "Tools and Technology" from the five data governance disciplines as an example, we can see how going through this exercise can add value to a Lean Data Governance agenda:

  1. Specify Value as seen by the Customer: Delivering value at the right time to the internal data customer is a crucial requirement for Lean Data Governance. Define value accurately by speaking to your customers. Liaise with business users of the systems and tools. Are your internal data customers getting what they need from their tools or are they undertaking additional work to get to the end result? Understand outputs and ensure that these are in line with what the internal data customer needs.
  2. Identify and Create Value Streams: Value streams need to be investigated and refined in order to make the overall Lean Data Governance initiative free of wasteful activity. Create new value streams if the old ones are not sufficient. Map the activity and the processes users need to go through to retrieve data and information. Pinpoint and refine the process flows to prevent wasteful activity and wasteful outputs.
  3. Make the Value Flow from Source to Customer: Flow enables the value to be delivered with minimal stages and activities. A seamless flow is a key requirement for Lean Data Governance. Investigating and mapping the flow of information and data within the tools helps identify unnecessary breaks and manual intervention. Eliminating these stages and introducing automation is a critical component.
  4. Create Pull: The internal customer must demand before you create the supply. Ensure your tools and technologies are not producing excess information or outputs when they are not requested. This will help eliminate waste in terms of people's time and resource such as processing and storage facilities.
  5. Strive for Perfection: Perfection is only achieved when feedback is received and tweaks are made. Consistently review the performance of the tools and technology by implementing a schedule to review the process. The business needs are ever-changing and the tools used to produce correct outputs need to be adaptive in order to facilitate these changes.

Applying a lean methodology to all five disciplines of Data Governance can lead to successes in overall data management, data quality and the flow of data within the business. After all this approach demands that the business engage with its key stakeholders to identify areas where waste is occurring, eliminate the inefficient or redundant processes, and prevent it from occurring in the future. Lean Data Governance can also deliver value to the internal data customer which in turn benefits the external customer base as they receive a service that is fuelled by a well-oiled and perfectly tuned Lean Data Governance Machine.

The hub of any organization is data, but data is an effective asset only when governed and managed meticulously. By taking a lead from the lean processes implemented at Toyota and adapting them to the realm of data governance, your company can truly maximize data to fuel business processes and business results.

More Stories By Kiran Gill

Kiran Gill is a Senior Strategic Consultant with Trillium Software. She has over 13 years of experience working within data management across various sectors. She has a strong background in data strategy and management as well as leveraging of data for marketing and performance measurement.

She has focused extensively on business process improvement, data quality, data governance best practice and maximising the utility of business data. Kiran advises clients to enable transformation, optimisation and maintenance of their data assets. Her work covers disciplines such as applied data governance, regulatory compliance, global data management, single customer view and online/offline integration.

Kiran has expertise in creating and overseeing large scale data management programmes within both the Public and Private Sectors. She has helped global businesses create aligned strategies to facilitate robust data management and data usage. She has worked with Healthcare, Automotive, Financial, Legal and Technology organisations to embed a data driven approach with an aligned business strategy.

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Most Recent Comments
Fatima.Zimichi 07/30/13 04:43:00 PM EDT

Great article Kiran!

I strongly agree with your idea that data management must be huge if great companies like Toyota stay disciplined, organized, and ahead of the game with it. Having an organized effort to collect information about topics of concern can increase subject awareness to a level of significant strength. That’s really how companies can compete with information.

My team at clearCi recently published an insightful white paper that tackles big data and information management. You can check it out here.