Roles

Building Your Data Governance Team (Keys Roles and their responsibilites)

Key Roles and responsibilities can belong to many people or one person can have many roles, it really depends on the size of your company as well as the culture. The ideal person to lead the Mindful Data Governance initiative is the Chief Data Officer (CDO) if one exists in your business, other wise select the best senior level employee that will be the ideal data evangelist to represent Data Governance and implement the Mindful Data Governance Initiative. Remember Data Governance is a team effort but the roles of each of the other members of the data governance teams are different but interdependent on each other. If you think of the roles of data governance as positions on a soccer team, it is great to know who are the strikers, midfielders, defenders, and the goal keeper so the team is unified but everyone has a different role to play in the team. I do want you to remember when implementing the Mindful Data Governance Initiative that their is no title changes for the employees that are assigned to these roles as the responsibilities. These responsibilities should not take up much of the employee’s time and become part of the ever day life and culture.

Data Steward
Data stewardship is a functional role in data management and governance, with responsibility for ensuring that data policies and standards turn into practice within the steward’s domain. (Domain = Data that is collected within their subject area).

Specific Accountabilities:

  • Implement data standards.
  • Ensure that staff who maintain data are trained to follow standards.
  • Monitor data quality.
  • Work with technical and operational staff to create a process for identifying data entry errors and correcting the data to match business standards.
  • Report to the data owner any issues that may require larger action on behalf of the business’s data governance structure.
  • Handle inquiries about data.
  • Receive and respond to any inquiries related to data that originates from the area they oversee; e.g., questions regarding access, standardization, organization, definition and usage, etc.

Data Owner
A Data Owner is concerned with risk and appropriate access to data. In comparing these two roles, often the data steward doesn’t care who uses the data as long as they use it correctly. Often the steward wants a lot of people to use the data! An owner, however, is concerned with who can access data, and tends to be more conservative with granting access. There is a natural conflict between these two roles, but in some organizations the same person plays both roles.

Specific Accountabilities:

  • Approve data Glossaries and other data definitions
  • Ensure the accuracy of information as used across the Enterprise
  • Direct Data Quality activities
  • Review and Approve Master Data Management approach, outcomes, and activities
  • Work with other Data Owners to resolve data issues and lack of harmony across business units
  • Second level review for issues identified by Data Stewards
  • Provide input to the Data Governance team on software solutions, policies or Regulatory Requirements that impact their data domain

Data Custodian
Data Custodian manages the actual data. This role manages servers, backups, or networks. This role may provision access per the data owner’s rules, and this role has mastery of a data schema and lineage. In comparison with steward and owner, a custodian has little knowledge of the types of decisions that are made using the data. In other words, a custodian knows exactly where data is located but does not know how to correctly use it.

Specific Accountabilities:

  • Provide a secure infrastructure in support of the data.
  • This includes, but is not limited to, physical security, backup and recovery processes, and secure transmission of the data.
  • Implement data access policies.
  • Grant access privileges to authorized system users, documenting those with access and controlling level of access to ensure that individuals have access only to that information for which they have been authorized and that access is removed in a timely fashion when no longer needed.
  • Ensure system availability and adequate response time.
  • Install, configure, patch, and upgrade hardware and software used for data management, ensuring that system availability and response time are maintained in accordance with university policies and/or service level agreements.
  • Participate in setting data governance priorities.
  • Provide details on technical, systems, and staffing requirements related to data governance initiatives.

We used the above labels to identify the roles and responsibilities of the team members of the data governance but these labels can be changed to fit your business better. The important part here is the understanding that there is a specific responsibility for each of the roles no matter how you label it. These three roles take up the majority of the work for data governance so having a clear definition will help the person that is assigned to this role exactly what their responsibility is. In smaller businesses, the same person may play all three roles. Even in large business, sometimes the steward and the owner are the same person. Because of the particular nature of each role, it is helpful to articulate each role even if they are assigned to a single person. Each role makes particular types of decisions and brings a particular perspective and skill set to governance work.

Putting each of these roles descriptions down on paper and personally communicating that roles responsibility to the individual, will help that individual perform the role successfully. Formally assigning roles makes it easier for colleagues to approach an individual playing a particular role and ask for assistance.

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

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

Benefits

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.

Analyzing unstructured data

Before we start to talk about unstructured data (or unstructured information) lets define it. Wikipedia defines it as information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents. I think that is pretty spot on. Today more than ever businesses are collecting more and more unstructured data whether it is from the vast social media sites to the enormous volumes of emails that are being sent out and received every day. There is a lot of great data just within social media sites and emails that are being collected and desperately need to be analyzed. This new data can be stored in a relational database as well as a NoSql database but my suggestion to you is store this data where you are storing all the other data sources — your warehouse. For example, if you are using Salesforce’s Pardot as your marketing tool you will have all your business’s campaign data, visitor data, and prospect data streaming from Pardot to your warehouse. Now you created a post in Facebook, you set up a campaign in Pardot and then you pushed an email out to all your prospects with a link to that post. You now know who opened that email and you know who clicked on the link. Please tell me how important would it be for your business to know the sentiment of all the comments that the prospects have left on the Facebook post? If you have not thought about that trust me it is powerful and it will make your data actionable. To analyze this unstructured data one of the best ways is to use Natural Language Processing (NLP). NLP is a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements and more. NLP is being used more and more and is driving many forms of Artificial Intelligence (AI). Think about it — you can decipher the sentiment of all the comments left on a post and based on the sentiment you need to pivot because the post has a negative sentiment towards it or better yet you do not have to do anything because the posts results have a positive sentiment around them. Not only is that information extremely important to your marketing teams but you will know how your product’s message is being received by the public.

There are a several important data points that you can get from NLP including sentiment analysis, keyword extraction, syntax analysis, entity recognition, and topic modeling to name a few. We are going to touch a little bit on each of these to show you not only how important the information can be for you and your business but to also make sure you have a general understanding of each of the topics. I utilize AWS comprehend which is a natural language processing (NLP) service that uses machine learning to discover insights from text and provides all the above functionality and returns the result in JSON format to either store in a database or display in real time inside an application.

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

Who and what does no business intelligence solution impact

Having no business intelligence solution in place impacts many things and many people. It impacts your bottom line, it impacts your customers, it impacts your employees, and it impacts the understanding of your business right now and in the future.

Your bottom line

With having no business intelligence solution in place, a business cannot see the full picture on what is happening. The business cannot see where all its money is being spent, it cannot see what areas are most inefficient, they cannot see what area they are most efficient in and why and then drive those efficiencies to other areas of their business. They slow down and prevent the business from making real-time data driven decisions. Without seeing the big picture, the business can be limited or just wrong on actions that are taken that they believe are good for the business. All of these reasons as well as many more impacts your business’s bottom line.

Your customers

Having no business intelligence solution does impact your customer. I get a lot of feedback on this one and it is usually from the people that do have the silos of data running within their business. They want to feel better by giving me excuses on how not having a business intelligence solution does not impact their customers. I always tell people that if you cannot get a full picture, a full understanding of the journey a customer takes from the start of the relationship to the end of their relationship within your business you sir or mam will never see its full potential. With so many touch points that a customer could have with a business you have the potential of creating many silos of data. These silos can be in reference to marketing, selling customers additional services, interaction with customer support and even the customer on-boarding process. All these systems and any others should be integrated in order for you to get a clear understanding of the experiences your customers will go through. The silos of data, the lack of the single source of truth will hamper that understanding and the business will never understand the customer’s journey. Picture this: a stack chart and on the X axis you have date values representing the last two weeks, and on the Y axis you have a numeric representation of hours. Within the charting you have multiple columns (different colors) representing different touch points that a new customer had to take in order to become a customer and then received their service from your business. Within seconds you can see where you can improve the process and where the process is working. That is just one powerful reason on how business intelligence can impact your customers and having a single source of truth can help your business.

Your employees

How does no business intelligence solution impact your employees? Without a business intelligence solution, you have silo’s that can affect employees when other departments within your business do not wish to share their data. Think about how many times have you heard or have been involved when a department within a business has identified a problem but cannot do anything about it. I have seen a business identify a problem and could not take the appropriate measures to correct that problem because of silos of data. This can be corrected by doing several things. First, leadership must create a unified front and be creative and tactical in their approach. Work towards a common goal. I know each department has its own responsibilities but the business should have one shared vision — the business’s mission statement. Create the data governance group because that will encourage collaboration, build repeatable processes, share measures across the business, and the group will act as one team pushing to that common goal. Most humans instinctively will get behind a common goal and will feel more united when they can share the same measure of excellence to be obtained with the person next to them.

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

One reason why more businesses are not building a Business Intelligence Solution

I have been asked many times by my clients and by peers why do you think more business are not implementing a business intelligence solution. It is a great question and I am sure many executives, directors, and managers have discussed this in reference to their own businesses and have their own reasons why a business intelligence solution never got started.

One of many reasons is there is no understanding of data that they are collecting. I have found that some of the businesses that I have dealt with do not understand what data they are collecting, why are they collecting that data, where that data is being stored, and when they are collecting it. To the what data, before business start to collect any data from any source (ERP, CRM, financial, web site, etc), I think they have to ask themselves are they collecting the right data? Remember that data can and should be analyzed to make sure your business is not only going in the right direction but help maintain that direction in the future and shift or pivot if need be. Asking solid questions is one of the primary ways of collecting data. As author Edward Hodnett noted, “If you don’t ask the right questions, you don’t get the right answers.” Questions asked in the right way often point to its own answers. Asking questions is the A-B-C of diagnosis. “Only the inquiring mind solves problems.” You are probably saying to yourself that is easy. Well it is easy but like everything else if you do not keep practicing your skills in asking questions you will never get better.

Like most businesses their primary business is most likely not collecting data. Sure, great questions are being asked and are being pinpointed to the problems that your business is trying to solve. You will find your data will be more useful and meaningful to what the mission the business is trying to accomplish as well deriving actions from a business intelligence solution. Identifying all the reasons why a business needs data upfront will not only help with the reporting of that data but significantly with the data collection. If you understand fully all the factors such as location, time, internal and external resources, vendors, etc. you will have the WHY knowledge of the data points that are important to the business and the data you will be collecting will be extremely valuable. The last thing that a business wants to do is to repeat the data collection process over and over because they have failed to think about the data factors in the beginning. Not only will this add time to obtaining valuable information but the data that you have been collecting might have pointed the business in the wrong direction.

Once you have defined the factors, developed the questions, and you are now collecting that data, where are you going to store it? Say you are a business that is collecting data from multiple applications which are important to your business such as custom web application, a ticketing system that helps customer service, and you have some sort of external data coming into a SFTP server. Each one of these applications are storing data in their own way whether it is contained in a data repository or in a flat file. Yes you can extract information from each one and get some type of analysis out of each source of data that can help the business but think about the insight you can get if you could join the data. Each one of these applications or process is a silo and does not know the other exists. Let’s take for example that your custom web application is used to manage projects and crews. Then you have data feeds from an external data source that has data from vendors related to this project. Your project manager looks at one set of data points from the web application and another set of data points in a spreadsheet application. That project manager then has to merge the data manually in some way like a spreadsheet. Moving the data is usually done by copying and pasting the web application data into another tab of the spreadsheet that they are viewing the vendor information from. Once they are done with that they now create another tab to merge the data points they want to see together. Then they will create another tab for graphs and calculations. Think about all the disparate source systems that you have in your network and then picture yourself repeating the above process in spreadsheets over and over. It is error prone, it is not scalable, and it is unmanageable. If that information is invaluable to the business then you need to kick start your business intelligence solution and then watch your business get meaning from that information with one simple point and click of a mouse. I can tell you one thing that a business does not want and that is having each one of their employees being their own data collector (their own single source of truth) because the business as a whole will not be able to identify any of the areas of the business’ shortcomings as well as the areas that they are excelling. The business is flying blind and are making decisions without seeing the full picture or the wrong picture.