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.



Benefits of Business Intelligence Visualization Tools

There are so many business intelligence visualization tools available in this day and age. Tableau, Power BI, Qlik, and AIR Intel are just a few. These tools will help the users make better and more informed decisions around their business by depicting the data in a graph or chart representation. There are many benefits in using a business intelligence visualization tool and we are going to touch on a few:

• Easier to understand and quick to action: The human brain tends to process visual information far more easier than written information. Use of a chart and/or graph to summarize complex data ensures faster comprehension of relationships than cluttered reports or spreadsheets.
• Interact with data: The greatest benefit of data visualization in my opinion is that it exposes changes in a timely manner. But unlike static charts, interactive data visualizations encourage users to explore and even manipulate the data to uncover other factors. Drilling into a chart to see the underlying data which could be yet another chart or a table/grid of the raw data can assist the user in seeing the data from the highest level to the lowest. For example, we have a pie chart depicting counts of sales calls by region within a specific time frame. You can then click on a region and then see a bar chart, each one of those bars represents a count of the amount of calls each sales person did in that specific region. Then that same user can click on a specific salespersons bar from within the chart to see all the details behind the calls: Who they called, when they called, how long the call was, comments from the call, etc.. This type of functionality is allowing the user to see not only how the sales people are doing overall but allowing you to see who are the best sales people making calls and why are they successful. Are all the successful calls made within a certain time frame? The visualization tool allows you to convey a story easier.
• Creation of new discussions: Another advantage to data visualization is that it provides a ready means to tell stories from the data. Heat maps can show the development of product performance over time in multiple geographic areas, making it easier to see those products that are performing very well or those that are underperforming. With this functionality built into most visualizations tools users (Executives, managers, and employees) can drill down into specific locations to see what’s what is working and what is not and pivot if needed.
• Communicate more effectively: Gone should be the days where you read an eight to ten page document to decipher the findings of what occurred in your business by the month and/or quarter. Now you can supply reports that can decipher complex data into simple outputs and have them automatically delivered to the people that should be reviewing the data. Not only do visualization tools allow you to communicate more effectively but I would also state that the reports are automatically delivered in a more timely manner.
• Absorb more information easily: Data visualization enables users to view and understand vast amounts of information regarding operational and business conditions. It allows decision makers to see connections between multi-dimensional data sets and provides new ways to interpret data through heat maps, bar charts, line charts, bubble charts, and other rich graphical representations. Businesses that use visual data analytics are more likely to find the information they are looking for and sooner than other companies.

Above are just a few of the benefits of data visualization tools and I am sure you can think of several more if you have played around or have used visualization tools.

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

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

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.


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

The importance of a Data warehouse in a Business Intelligence Solution

I consider the data warehouse as the foundation of a business intelligence solution – your single source of truth. As with any good structure if the foundation is not good the structure will come crumbling down. Before we go any further about the importance of a data warehouse in a bi solution lets define it. A data warehouse is a large repository that contains data that is collected from a wide range of sources. These sources of data are usually the business’s transactional systems. The data warehouse is used to guide decision making at all levels and is optimized from read access. Taking the time here to build your warehouse by defining your schemas and tables  is crucial to the solution and the business. You do not want to rush through this part by any means. By not designing the data warehouse correctly the project’s cost can increase significantly, cause delays as well as potentially incorrect reporting of the data that is being stored in the warehouse incorrectly. Take your time here to ask the right questions, develop the right solution, review the solution with your team, and then implement and test. You will need to make sure the data warehouse is not only designed right but can also grow and scale as your business’s data needs grow. One thing you do not want to happen is in six months to a year  you are getting reports that the system is either slow and/or unresponsive. If one of my clients were asking me about this situation, my first thoughts would be around did the team calculate the right size warehouse or server, did they miss something within the database structure (wrong join, wrong columns were index, etc.). I can not say this enough, the data warehouse is the foundation of the business intelligence solution and it deserves the time to be designed, built, and tested correctly. Like I stated earlier if the foundation is bad then your business intelligence solution will have many issues, if you do not do your due diligence around the building of the data warehouse your solution will have a lot of issues in the future.

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.