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

Descriptive, Predictive and Prescriptive Analytics

What I have been seeing with all my clients over the last three years is them trying to get their arms around their data, cleaning it, gathering it into a central location which then they typically create dashboards and reports to see how their business did in the past but some are looking at how they are doing right now. So, the way most of my clients are looking at their data is called descriptive. Descriptive data analysis gives businesses insight into the past. Descriptive looks at the data, summarizes the data and then interprets that data into human readable format to give us analytics of the past. The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes). Most often, the underlying data is an aggregate or count of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like total stock in inventory, average dollars spent per customer and year over year, or even change in sales.

When I talk about Predictive data analysis I am looking to understand the future. Predictive analytics want to look at the data and then predict what can happen in the future. Predictive analytics want to give actionable information to its owner on what could be coming. Currently there is no predictive data analysis that can give you with a 100 percent accuracy on what the future holds. A business should take and read the results on what might happen in the future and decide on the path based on that knowledge.

These two statistics — descriptive and predictive — try to take the data that you have, and fill in the missing data with best guesses. They combine historical data found in CRM, ERP, HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets. Businesses use predictive statistics and analytics anytime they want to look into the future. Predictive analytics can be used throughout the organization from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. These statistics also help to forecast demand for inputs from the supply chain, operations and inventory.

The last analytic option we will talk about is prescriptive data analytics. Prescriptive data analytics is when you want to be guided on all the possible outcomes. The relatively new field of prescriptive analytics allows users to “prescribe” a number of separate actions to and direct them towards a solution. These analytics are all about providing direction. Prescriptive analytics attempts to quantify the effect of future decisions in order to advise on all the possible outcomes before the decisions are actually made. When prescriptive analytics are at their best it will help predict not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions. With this type of decision analytics, support business should feel comfortable with the actions that they need to take, either staying the course or pivoting to right the ship.

Which analytics does your business need? Does your business need descriptive, predictive and prescriptive data analytics? I believe in order to answer that question the business needs to know how advanced of a business intelligence solution it needs in order to be successful. In understanding how each descriptive, predictive and prescriptive and what questions they can answer for the business will drive the business to implement a simple or more complex business intelligence solution. One piece of advice that I would like to give here is start off with the simple solution and once that solution is providing the information you need, then enhance your business intelligence into a more and more complex solution. I believe taking this approach will give you a much higher success rate of implementing your business intelligence solution as well as a higher user adaption.

To quickly summarize the last three paragraphs, descriptive as we know answers the question of the how it looks at data in the past. We also reviewed predictive where we talked about how it will most likely answer questions on how something might happen. And lastly prescriptive will give you answers to questions on what actions can happen. Depending on your business goals and what answers you need from your data, the decision on if you need descriptive, predictive and prescriptive data analytics is very personal to you and the business.

I think it is important to show you the different levels of human input to draw conclusions from descriptive, predictive and prescriptive as well how each analytic area answers which questions. This will give you a good sense of employee time that will be needed depending on the way you will be looking at your data

Being a data driven business

Once you start to understand your data and the information is available throughout the business, a whole new world will open up to everyone that has access to the data. You will be able to see not only how the business is doing as a whole, but how each department of the business is doing as well as how employees are doing personally. With the same information (your single source of truth) being accessed across your business and the same story being told to everyone, not only is that powerful because you now have everyone seeing and reading the same data but the entire business is now moving in the same direction. To keep the business moving all in the same direction, to share a common goal, and to make sure all departments are doing what they need to do is extremely important because this will help drive up revenue and this will lower cost by seeing which areas within departments can be enhanced. With all the great information that you start to see, do not ever be satisfied with what your data is telling you. I think the most important thing that a business can do once the business intelligence solution is in place is not stop asking questions around the data. I know I have had many people state to me if you understand data then you can make smart decisions. Understanding the data is really beneficial and really helpful in guiding your business at that moment, I am talking about not being satisfied with all the results that you are getting now from the information the business is seeing. What I am stating is for you and others to keep asking the questions that I have suggested below. You might remember these questions from elementary school in regards to a English class. By asking these questions, you will keep moving the business, the department and the employees in the right direction.

Who: Who is the data about? Who is collecting the data? Collecting data across multiple who’s will give you a firm grasp of what is going on across multiple who’s and not a single who.

What: Of course you want to know what your data is about. Know what makes up the data and know what makes up the numbers behind the data.

When: Have a firm understanding of when your data has been or is being collected. Most data that is being collected is linked to a timeframe and knowing the time of your data is important to what decisions you make now or in the future.

Why: Knowing why the data has been collected can help you understand what needs to be shown to help the business. The constant asking of why this data is being collected will keep the data in check to make sure it has meaning and not tied to some crazy agenda that someone has gone of on. If it is tied to an agenda be cautious and keep asking questions.

How: The last question is how. How was this data collected? If you are purchasing or using an external data file you need to know how this data was collected and/or aggregated. Do you feel comfortable with the data, do you trust it?

Starting

Getting ready to start a BI Solution?

Before building out the plan for a bi solution project, I always like to think about what other projects that I have done or know about that are similar to the one I am going to embark on. I always ask myself what issues or obstacles did I encounter in those projects? There are so many reasons why projects get dragged out or just do not succeed, either from the start or they dwindle out if the project exceeds its expected delivery date. Lets just review some and see how many you came across in your professional career on why a project has failed.

 

  • No Executive sponsorship. There is no real buy in from the senior management team, which can lead to many different problems for the success of the project. You have to be persistent here and spend time presenting and socializing how important this project is to them and the company.

 

  • Battle between departments. Believe it or not I have run into this issue so many times when I have investigated why so many companies have failed in implementing a business intelligence solution. So what I mean here is that each department is acting like a ten year old child that does not want to share their data or their process because they believe it is their own secret sauce or they have something to hide.

 

  • No real project plan in place. This is were a company decides they would like to shoot from the hip on this project. They have no real kick off, no steps, no definition of what success is for the project, no anything. They fail to realize that this is a company wide project and the need for a project plan not only holds everyone accountable but ensures the right steps are completed before the next step are taken. It is this mentality that will kill any project almost from the start,

 

  • The IT department wants to build everything from scratch. This can be a problem in a lot of companies were the IT department wants to build everything custom. This can be not only very time extensive but also very costly. I always tell my clients to take the build, buy, or align approach when looking at the different areas of a project. There are several reasons when you should consider building a custom application over buying or aligning: Off the shelf products cannot meet every need, off the shelf products are to rigid, off the shelf products may not be compatible with your existing applications. Now let me give you several reason when you should be thinking of buying an off the shelf product: budget is limited, lack of time, lack of technical proficiency, and technology would not give you a competitive edge.

 

Above are just a few examples why your project could take longer than expected to be completed or just fail. I am sure that you can think of many more or even have experienced many more. I know in my long career as an IT professional I have come across reasons why projects have failed that would make people laugh and then some other reason that would make people cry. I will save those stories maybe for another blog. I think the main reason why I wanted to get you thinking about project failure is so that history doesn’t repeat itself and I am a firm believer that if you can learn from all failures, you will have a more successful future.

Building out a Business Intelligence Solution? Make sure you document your data collection process

The first step of building out a business intelligence solution is that you have identify all the data sources within the business. It is right after you identify the datasources is that you want to develop  and document all the data collection processes. Why develop a data collection process? Not only will creating a data collection process standardize the way you collect data for all the groups from within your business but it will ensure the integrity of your data is kept very 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 for the business and the business had no idea how it was being collected and that employee left, well I think that would have some impact on the business. The business most likely will 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? I think you are understanding the point I am trying to make.  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 in the now and in the future.

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

  • Give 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
  • What are the measures and/or reports that will be built from this data

Even if you currently have a business intelligence solution in place but do not have all your data collection processes documented, go back and start getting them documented as soon as possible.

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
Silos of Data

Silos of data

What are silos of data? I define silos of data as points of data that have been collected and being stored in many locations that could bring value to your business. Many times these silos of data store similar to identical information. Usually these systems are considered transactional systems that employees and/or customers are entering data into every single day. These transactional systems typically have different databases that are storing data related to your business and can be hosted on and off premise. Other silos of data that you will need to consider are spreadsheets, word documents, text files that are being maintained by employees on their personal computers. Employees (which includes everyone from the CEO down) are most likely storing data that could be important to the business. Identifying and coming up with a solution to collect all that data will not only help give you the big picture of the business but also protect one of the businesses greatest assets their data.

I would like you to think about not only where all the silos of data are and how valuable it would be once connected but also be thinking about how that data is being protected and who else is viewing that data (if that does not keep you up at night, I do not know what will).

I typically tell my clients, ask yourself two questions every time you walk out your business doors. Is the data on my laptop important to the business and/or sensitive in nature? If it is, how is that data being protected on my laptop? This is an area were I think a lot of businesses fall short and not only do they not have policies in place but do not even think of this situation. I have seen so many times employees storing sensitive information on their laptops, walk out their companies doors without even considering the security and responsibility that they should have in protecting that data.

Why you need a Data warehouse

Let’s start off by defining Data Warehouse. A data warehouse is a central repository of information that can be analyzed to make better-informed decisions. It can also be further defined as a repository that stores large amounts of data that has been collected and integrated from multiple sources – such as a CRM, payroll or accounting software, or inventory and sales systems.

“Why do we need  a data warehouse that is separate from our business transactional systems?”  This is a question that we get asked frequently at CopperHill Consulting. We answer this with several statements:

  • Business transactional systems are built for tasks and very specific work flows.
  • Business transactional systems allows editing, while a data warehouse is read-only.
  • Business transactional systems should only hold current data. A data warehouse can hold historical and current data.

 

If you want to move forward with your BI strategy, you need a data warehouse.

The data warehouse is a core component of Business Intelligence. Here’s how a data warehouse makes an impact:

  • Maintains a copy of data from your transactional systems. This allows you to keep your transactional systems lean and processing quickly with only the most recent and relevant data visible. It also lets you keep a history of all past transactions for recordkeeping and analysis.
  • Improves the quality of the data. Identify duplicate entries and records. Find anomalies in your data. Build custom views. These are all ways that a data warehouse can help improve the quality of your data both in your transactional systems and in your reporting.
  • Restructures information for different users. Create different user roles to restrict permissions and set different views to make it easier for users to understand the story their data is telling them.
  • Integrates data from multiple transactional systems. This lets you see a bigger and clearer picture of your business across all departments and silos.
  • Delivers excellent query performance without compromising business transactional systems. No need to worry about your systems slowing down, timing out, or crashing.

Your data warehouse will change and evolve as your business gets larger and greater over time. As your company grows, your requirements shift.  Your data warehouse needs to be designed to be flexible and scalable so it can handle changing requirements. Automated integration solutions to move company data from your business transactional systems and flat files to the data warehouse is one way you can make sure your data warehouse can grow with your company. Automations keep costs low as well as lower the chance of errors.