Best Practice

Define Your Mindful Data Governance Initiative Best Practices

By defining and committing to best practices within the Mindful DG Initiative is critical to the success of the initiative for your business. To help build your best practices for your Mindful DG Initiative think of the final results and what that looks like. Knowing what that final results and goals looks like ensures that the path you select to get there is correct. Here are some sample goals and results:

Improve data security
Remove the collection of duplicate data
Use data to increase business profits
Make consistent, confident business decisions based on trustworthy data aligned with all the various purposes for the use of the data assets within the enterprise

These are just some examples of goals that you might be thinking about as a final result for the Mindful DG initiative. It is a very good exercise to go through and I do highly recommend that you do it, as it will give you a good vision of what the initiative will accomplish at the end.

Define your best practices for your business. When going thru your best practices and deciding on which best practices that need to be in place for your business ask yourself two questions when deciding. Is the practice applicable to do and can it be done at your business? Will the Mindful DG Initiative be at risk if the best practice is not done?

If you do not answer yes to both of these questions then the practice that you are defining is not a best practice and should not be implemented in your business. Remember businesses will share some best practices but not all best practices will work for every business. Sample best practice should be put into place.

Will the lack of communication and buy in by the Senior management cause the initiative to not be successful?
Will are Data governance be at risk if Senior management does not support or buy into DG Initiative?
Data Governance principals will be established and used daily through out the business?
If we do not establish quantifiable measurements will the DG initiative be successful?

The best way to complete the process of building best practices is conducting interviews of managers in the different business departments. Involving them early in the best practices not only gives them the feeling that they are part of the initiative but they are helping build the initiative which gives them more feelings of ownership. Prior to meeting with them one of the best things you can do is forward any best practices that you already have in place, as well as the two questions that you should answer yes to in order a statement to become a best practice. This will help keep your meetings short and focused.

Once you have the best practices in place you can start to implement them in phases, remember we want to convert the nonstandard data governance to a formal data governance. Not only will this help you see how your best practices are working quickly but will also allow you to evaluate them and see if they need to be improved.

5 Levels of Mindful Data Governance Initiative

The Mindful Data Governance Levels

Quick Overview of Mindful Data Governance:
In my previous blog I went over why I decided to create Mindful Data Governance and the meaning. Now I would like to go over the different levels of Mindful Data Governance as well as the first step. Within Mindful Data Governance the first thing a business would do is a self evaluation questionnaire to know exactly where the business is starting from, prior to the initiative kickoff meeting. This self evaluation will allow the business, as long as they answer the questions honestly to see what level (or state) their departments and/or business is in before implementing the Mindful Data Governance Initiative. Remember implementing Data Governance does not have to be a hinderance, a distraction or even time consuming. Lets get to the levels.

The Level of Mindful Data Governance Initiative:
So after much thought I came up with the five levels Unknowing, Acknowledge, Acceptance, Mindful and Enlightenment. I will go over each level in detail so you know exactly what each one means.

1 – Unknowing: There is no data governance, security, accountability or ownership in place. There is no informal standards that is known, what this means is that everyone in the business is doing their own thing. There are no business glossaries, no metadata management, no data models existing in the business. Information is fragmented and inconsistent throughout the business systems. Business Decisions are made with inadequate information or with no information at all. Your business does not treat its data as an asset and the business is undisciplined and very reactive. There is most likely duplicate and inconsistent data being stored.

2 – Acknowledge: The business starts to become mindful for the need to control the inconsistent information and do something about the poor data quality. The lack of data ownership and lack of executive support has become evident. The acknowledgement for the need for tools, processes, policies, and standards have been made. The business starts to understand the value of quality data that can be shared and used across the business. Your business recognizes that there is a cost to enter data into multiple systems. Employees are still being utilized to manage and move data. Business has also acknowledged that there is redundant data.

3 – Acceptance: The business understands the values of quality data that can be shared and used across the business. Data is starting to be shared across transactional systems and departments. Data governance polices and standards are be created but following them is almost nonexistence. The majority of the work that has been done within Data Governance is around the retention of data. Business has processes in place but some departments remain separate from others. Formal data management documentation is building. Vision and data strategies have been defined and implemented. Metrics and standards are transpiring around the use of the data

4 – Mindful: Data is being viewed by the business as a top asset. Data governance policies and standards are developed, circulated and well understood throughout the business. A governance body is in place to resolve cross-departmental data issues and they are identifying best practices that should be implemented through out the business. Roles and responsibilities are assigned, they are being followed and data quality, security, usability, and accessibility are increasing through out the business. A formal training for on-boarding new employees in place to ensure quality and standards are met day one.

5 – Enlightenment: The business recognizes that the data that is being collected give them a competitive advantage and it is used to create value and efficiencies through out the organization. Data Management and Data governance are seen as a daily part through out the business. Service level agreements are in place and are enforced. The business has achieved their goal in Data Management and Data Governance. Overall data management if fully aligned and in place and supports the business’ performance. All business’ processes are automated and repeatable. Data management Roles are well established. Monitoring off data is in place and metrics and audits are used to continuously improve data quality.

Below is the questionnaire that you can take to see where your business is at and where you can move up to. To get your score, simply sum up the values of your answers and divide that answer by 13 and you will have your average, take your average score and match it to the number next to one of the levels of the Mindful Data Governance Initiative above.

Mindful Data Governance Initiative Questionnaire

How does your business feel about your data as an asset?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

How accessible is the data that is required to make decisions for your business?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

How is your data quality (Example duplicate data, completeness of your data, etc.)?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

How integrated is your data sources?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Is your data storage and security ample for your business?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Is there a data warehouse in place?
1 – Non-existent
2 – Some apps have their own database that is accessible
3 – Data is pushed manually into the warehouse
4 – Some of the apps are pushing data to the warehouse (automated)
5 – All data collection applications are pushing data to the data warehouse (automated)

Is each of your data transactional systems documented (Processes and procedural)?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Is your data accessible from within inside departments that collect the data?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Is the data that is collected in each department accessible to other departments?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Are there policies in place around who can use data, how they can use data, which parts can they use, and for what purposes?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Do you have security policies and considerations need to be in place for each of the data sources? (HIPPA, SOC are just examples)
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

What is the attitude from your C-Level or Leaders in the Organization around Data Governance?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Does your C-Level or Leaders make decision based on the data collected by the business?
1 – Non-existent
2 – Poor
3 – Fair
4 – Good
5 – Excellent

Mindful Data Governance Initiative

Mindful Data Governance Initiative

Before I go into what Mindful Data Governance Initiative is, I do what to say that I truly believe that every business out there can get real value from implementing Data Governance. If Data Governance is not on the roadmap for your business to implement I would seriously start thinking on how you could get it on there with key messaging to the executives. In my opinion Data Governance was nice to have back many many years ago but now it has become a must have, especially with the amount of data that is being collected by business today and the importance of analytics.

What is the Mindful Data Governance Initiative?
Why did I developed this methodology that I call Mindful Data Governance Initiative, I wanted a methodology in implementing Data Governance for my clients that would not hinder much of my client’s employees day to day activities. I wanted a methodology that would not break my client’s budget and allow my clients to see the importance and value that Data Governance can bring to their business. Businesses are paying money for custom software, SaaS, and data feeds that are bringing important transactional data into their business why not make sure that data is of the highest quality, why not make sure the data is not being duplicated, and why not make sure the data is linked and being shared, and why not make sure that data is showing them the right picture on how their business is doing.

Let me just say this again implementing a Data Governance initiative does not have to take a large amount of dollars and does not have to be disruptive to employees day to day activities. This is why the methodology of Mindful Data Governance Initiative can impact businesses in a very powerful way, so businesses can start to reap the rewards of Data Governance and not have the worry about the impact of implementing a Data Governance solution could have on their business, their departments and/or their employees. The Mindful Data Governance Initiative is an approach that is done in phases with little impact to the business and starts to only address the nonstandard Data Governance items that the business is already conducting. We will go into what I define as a nonstandard data governance item very soon.

Why did I choose the word Mindful?
Merriam Websters definitions of Mindful are bearing in mind and inclined to be aware (conscious or aware of something). You can implement Data Governance within your business without creating a disruptive environment and impacting employees day to day activities by being mindful of the approach you take in Data Governance. Most business have some sort of nonstandard data governance that they are conducting and could implement standards around. Looking at implementing Data Governance this way (starting with the non-standards that you know exist) businesses can start their Mindful Data Governance Initiative easily and cost effectively. With this methodology, businesses can see the benefits of creating documentation and policies around those nonstandard tasks that have been identified and ensure the 4 principles are being applied to them availability, usability, integrity, and security.

Why did I choose the word initiative?
Lets look at the definition of initiative from Merriam Webster, 1. An introductory step. 2. Energy or aptitude displayed in initiation of action. Synonyms: action, drive, and ambition. I think when most businesses executives and sponsors think of Data Governance they do no think of it as an initiative, they think of it more as a directive that will disrupt and hinder their business’ day to day operations and could cost them a significant amount of money. They think of policies that are time consuming and costly to write and enforce in their business. That simple is not true with Mindful Data Governance Initiative. This approach can be very unobtrusive and budget sensitive especially because it can be done in phases.

Lets look more in-depth at the definition of initiative and why I choose that word to use in my methodology. The first definition of initiative is an introductory step. When implementing a Mindful Data Governance Initiative you look at everything as phases and do not worry about implementing the entire data governance all at once, as you do not have to implement everything to see the benefits and obtain a return on investment (ROI) of this initiative. When taking this introductory step (the first phase) the business and the executive sponsors will not only see a quick win but they will see why data governance is important. Before I tell you what I think should be the introductory step of your Mindful Data Governance Initiative let me explain that I think most business implement some type of nonstandard data governance in sections of their business’ and they just do not realize it. Here is an example of what I think is an nonstandard data governance practice: It is known by certain employees in the Customer Service department that when entering new products into their inventory systems that the name of the grouping of those products must match exactly the name inside their CRM system. Thats it and I believe most business will find a lot of these tasks undocumented within their organization, and the impact of not documenting these processes is that if these employees leave the business so does the knowledge. This should be your first step, identifying these areas and asking these questions: Where does the data come from? Where is the data being stored? Where is the data being used? How accurate is the data? Are these data points already being collected in other parts of the business? What rule(s) does the data have to follow? Who is collecting the data? These are just some of the questions and answers that should be documented, so business risk goes down and financial benefits increase.

The entire reason why I have developed the Mindful Data Governance Initiative approach is because I believe that Data Governance does not have to be a big impact on the business and can increase the value of a business’ data, decrease cost, increase the business’ revenue as a whole, ensures compliance and regulation needs are met, and promotes transparency through out the business. In my next blog I am going to talk about what are the different levels in the Mindful Data Governance Initiative and how you can find out what level does your business fall in. If you are interested in implementing the Mindful Data Governance inside your business, let me know I would love to help.

Self Service BI needs Data Governance

Self Service BI I think is very important for businesses to implement and can greatly increase productivity of a business as a whole. Let me give you what I think Self Service BI. Self Service BI allows employees to conduct their daily analytics work with little to no IT intervention which increases productivity and gets answers to questions that are important to that department or set of employees.

When business are looking into Business Intelligence Visualization tools to allow it’s different departments to conduct their own data analytics and data discovery, I will say Data Governance not only greatly helps but I would state it rescues this ideology that a business wants to put into place. Data Governance can help these self service users with complex data models and ensures that all the data that is being analyzed is of the highest quality. Data Governance also makes sure that all the different departments in the business are looking and talking about the data in the same way, so everyone is getting the same story from what the data is saying and moving in the same direction.

With all the above being stated, Self Service BI does come with obstacles and each of the below obstacles need to be addressed and can be solved with a Data Governance solution. You might encounter more obstacles as you pursue Self Service BI but the below ones are the obstacles I have ran into more then several times and wished to share.

Obstacle 1: Data quality. A lot of businesses that I have worked with realize that they have data quality issues once we get into the project. Trying to convince the stakeholders that Data Governance is built for exactly this is not only cumbersome sometimes but outright difficult. Many times the stakeholders say the investment is to much in dollars, my statement back to them is that I do not think you can afford not to put a Data Governance in place. Here are just an example of some questions that you can ask the stakeholders or executives. Is there duplication in the data? Can you see all the touch points of your customers? Is the data that your systems collecting accurate and valid? Letting the stakeholders know that implementing Data Governance does not have to be a huge investment in time or money if you take the approach I like to call the Mindful Data Governance Initiative.

Obstacle 2: Data growth: Many business are seeing an exponential growth in the data that they are collecting (variety and data sources) especially business with many departments. Implementing a Data Governance solution ensures data quality and that all the processes that collect data are repeatable and valid.

Obstacle 3: One fits all: In Self Service BI there might be an attempt that one tool fits everyones needs from a sales rep to a data analytics employee. The methodology of one fits all will work in Self Service BI but will not work with Data Governance as the wide range of data analytics is way to wide. However, self-service is of no benefit if the data being accessed is not valid and governed correctly. Data Governance provides a consistent and repeatable way to manage data collection across business units and to make sure that information delivered is reliable and is of the highest quality.

Obstacle 4: Although the biggest benefit of Self-Service Computing is that it offers complete democratization of complex Data Management tasks in the daily life of a business user, you still have to think: Are there any down falls of having so much freedom over critical business data? As all types of business users will have access to critical data, isn’t Data Security and Data Privacy at high risks of loss or corruption? Unless Data Governance policies take these risks into full consideration through the rules, procedures, and access controls, the whole purpose of self-service may be compromised.

Like I stated early you will definitely come across more obstacles when implementing Self Service BI in your business and you will have to figure out solutions for them. It is my belief that if you do not have a formal Data Governance solution in place first it will make getting over all these obstacles extremely hard if not impossible.

Data Governance 4 Your Business

What is Data Governance?
Data governance refers to the overall management of the availability, usability, integrity, and security of the data deployed in your business. A healthy data governance program includes a governing group, a defined set of procedures (that are repeatable), and a well-designed plan to execute those procedures (documented).

I always think of the number four when I think of Data Governance. Here is why: Some attributes of the number four are hard work, security, practicality, productivity, appreciation, tradition, solid foundations, security-consciousness, self-control, loyalty, conscientiousness, high morals and ethics. The essence of the number four is security, diligent work and strong foundations. All those attributes and the essence of number four is exactly how your data governance is to be implemented and treated. One of the most important attributes of a Data Governance is a solid strong foundation. Remember DATA GOVERNANCE 4 YOUR BUSINESS

Your Data Governance should consist of a four-way structure incorporating availability, usability, integrity, and security.

Why is Data Governance important?
Data Governance is important because it ensures that the data assets are formally, proactively, properly, and efficiently managed throughout the organization to secure its trust and accountability.

Data Governance comprises the collecting of data, revising and standardizing it, and making to ready for use. It makes the data consistent. Data Governance ensures that critical data is available at the right time to the right person, in a standardized and reliable form. This helps the business and its operations to be better organized. Adopting and implementing Data Governance can overall help improve the efficiency and productivity of an organization.

What are some of the methodologies of Data Governance?
Data is king and is so very important to every business no matter the size. You can implement Data Governance in phases but the implementation must always be across the entire business in order to be successful. Also it is very important to remember that once a phase has been implemented the governance body will have to continue monitor, maintain and review those implemented processes and the data, this is critical. Other success factors that can help you implement a winning Data Governance are: Look and prioritize areas of improvement (phase approach); Create roles, responsibilities, and rules from the processes people use in working with the data; establish an accountability infrastructure; convert your business culture to a master data management system. The way to start this highly structured and monitored Data Management strategy is to standardize the use of terminology across business units and enforce consistency of use. The ultimate goal of Data Governance is to make sure it is possible to consolidate your data and create a consistent view of that data across the business for advanced Business Intelligence activities. Literally you are turning data into a “Single Source of Truth” that the entire business is looking at.

Business Intelligence without Data Governance
Can you implement Business Intelligence without Data Governance? Of course you can. I believe that the two must go hand and hand. A sound Data Governance can significantly increase the returns of a company wide Business Intelligence investment. When starting a BI initiative with clients I seldom hear them talking about Data Governance but that does not take away the reality that it has to exist. Without governing your data in this data driven world business businesses will never realize the full potential of the data they are collecting. Data governance used to be a nice to have, but due to the increasing focus and importance of data and analytics, it’s becoming a necessity that helps to drive data management across the business.

For Example
Take a financial business I worked with that had very poor, inconsistent customer data. All of the customers with first, middle and last names had multiple differences, and addresses were inconsistent. This type of situation makes it very difficult to do any type of customer analytics, from identifying cross-sell opportunities to tracking and understanding customer experience. Data Governance can be a first step in identifying the issues, defining standards, and implementing changes in the business to align with these standards.

Remember DATA GOVERNANCE 4 YOUR BUSINESS

Developing a data collection process and documenting

When building out your business intelligence solution an important step of developing data collection processes and documenting those processes is critical to your business and its success. Why develop a data collection process? Not only will creating a data collection process standardize the way you collect data for all the groups in your business but it will ensure the integrity of your data is kept high. Processes will ensure that your actions are repeatable, reproducible, accurate, and stable. Think about it: if a business had an employee that was collecting critical data on the business and the business had no idea how it was being collected and that employee left, that would have some impact on the business. Would the business be able to figure out how the employee was doing this but after how long? At what cost to the business? Could there be repercussions? Ensuring processes are in place for your data collection will improve the likelihood that the data is secure, available, clean and the measurements derived from that data will help the business now and in the future.

There are many reasons why every business should be documenting the data collection process. If you are then documenting your processes becomes transparent and your data becomes comprehensible in the future for yourself and others. Your documentation of each of the data collection process should include:

• A good description behind the data that is being collected.
• Provide all the answers: the who, what, why, where and how of the data.
• Provide any conditions of the use of the data as well as the confidentiality.
• Provide any history around the project for collecting this data

Your Single Source of Truth

Purchase The Kindle Edition
Purchase The Paperback Edition
Your Single Source of Truth is a quick-read for busy business and IT professionals struggling to create a Business Intelligence solution. Packed with advice, proven methods, and real-world uses cases, this book provides the knowledge to get you not only started but to keep your Business Intelligence solution going.

This book is intended to help you understand how a business can deal with their epidemic data problems and see a bigger clearer picture from the data they are collecting. There are mountains of data being collected in many different departments each with their own transactional system (silos). And each silo is not being joined to give a bigger and clearer picture to the business. This is a data centric world and businesses are collecting and saving data at an enormous rate but most are doing nothing with that data. They are not learning from the data and not making actionable and informed decisions from the data.

Business Intelligence and silos of data is not just a small business issue — it’s an issue that all different size businesses are facing and are having problems getting their arms around. Whether it is lack of resources, low priority, or a lack of understanding that there is a problem. I believe if I can explain the issue, analyze it and point companies in the direction in solving their Business Intelligence issues then I would get to see many businesses grow and flourish. I want to help businesses answer those questions that I believe every business wants to answer: How is my business doing right now? How is my business doing compared to how it did in the past? Are all my areas of my business performing well? Which areas can have better efficiencies? What are my customers thinking and how can I better serve them? This is just a very small sample of questions that I know a business intelligence solution can help businesses answer and this book will help get you started.

data governance

What is Data Governance

Businesses having a data governance committee is one of the most overlooked and undervalued areas when businesses are looking to start and/or maintain their business intelligence solution aka your single source of truth. I have fun exercise for you to do, one day go out and ask different friends and/or family of yours that are in different businesses (it could be a company with 25 employees to a company with thousands). Just ask them these two questions: Do you have data governance in your business? What is your definition of data governance? I think you might be very surprised by the answers that you receive from the people you have asked and especially if you ask people that are from different sizes of businesses.

I define data governance as the overall management (with the help of processes) to ensure the availability, usability, integrity, and security of the data that is employed throughout the entire business. I said it throughout the entire business. Data governance is a cultural change within your business and can be a positive change if it is approached and implemented properly. Data governance can be disruptive in the beginning but that does not mean it cannot be a positive disruption. You have to take the approach that this is an ownership of the data and each department belongs to that feeling of ownership and has a responsibility to themselves and the business to ensure that they are doing everything they can to follow the data governance guidelines. Empower the employees and you will see that data governance within your business will go along way.

Some key benefit that a data governance can give your business:

  • Better data quality
  • Better decision making
  • Increasing Operational Efficiencies
  • Improved Data Knowledge
  • Higher Revenue
  • Regulatory Compliance