Best Practice

Define Your Mindful Data Governance Initiative Best Practices

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

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

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

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

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

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

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

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


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. What level are you?

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 replicated and data 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

The power of integrating your systems

The first thing that I always hear from businesses is that they do not have integrated systems because of COST and the the effect of resource consumption. The biggest resource consumption that you can save on is your employees time. To be able to put an end to swivel chair technology in your business will not only save you time and money, but will ensure that one of your business most important asset (your business data) is kept valid and clean. Your business should be driving to get all your systems integrated as much as possible to leverage the existing processes, people, technology and information that you have. Security is another another important factor why system integration is important to businesses. If your business uses many systems in its day to day operations then integrating those systems should be at the top of your IT’s implementation goals.

What integrating your business systems can mean to you:

  • Better Management of Information: If your systems are not integrated it is impossible to get a complete picture of how your business is doing. When you have several systems that you are inputting data, some of those systems will be out of sync for longer periods of time especially during high traffic times of sales or as your business starts really to scale up. So what that means different managers in different departments that are using different systems are not seeing the same picture.
  • Higher Productivity: Dealing with many different standalone systems can consume time and with employees manually entering data into these systems (swivel chair tech) that is time be wasted when those employees can be redirected to higher priorities. Not only is this type of process time consuming but has the potential of creating bad data as employee can enter different information in each system. The scalability and expansion that system integration can give your business for growth can be achieved without adding more employees, so what does that mean. Well that means as your business increase so does your profitability.
  • Cost Savings: The return on investment (ROI) that you will see not having to manage the data input for multiple systems will be seen extremely fast, what you will also notice is that you data will be cleaner and all the different different departments will be seeing the same picture at the same time.
  • Greater Customer Satisfaction: Having multiple systems that are not integrated can mean that it can take extra time to fulfill your clients orders as well as respond to their enquiries or complaints. Nothing can increase customer satisfaction more like real time data flow between your systems.
  • Improved Security: Most businesses deal with some type of sensitive information that requires protection. By integrating your systems, you can easily build in the security tools necessary to prevent access by unauthorized users. Integrated systems will also help your business keep access to other systems to only the employees that need it, lowering the potential of issues and security threats.
  • If you are one of those businesses that is thinking can I really afford to integrate my systems, think about it this way can you really afford not to integrate. Integrating systems does not have to be done all at once, it can be a phased approach keeping cost a little more spread out while seeing the benefits and savings sooner.

    Unfocused Data

    Why aren’t business focusing on Data Governance and what does that mean to them.

    There are so many reasons why businesses are not focusing on Data Governance, but I think the main one is that the Executive teams really does not understand what Data Governance is and it’s benefits. Based on my experience and what I have been observing is that most businesses are focused on getting their systems (separate transactional system(s)) up and running so that their businesses are making money and they are processing their customers through some type of sales life cycle. I truly understand that mentality but your business does have to grow down the road. If businesses cannot fully understand the data that they are collecting from those transactional systems and how it ties back into their business strategy, I believe they are building a black hole within their business.

    Another big reason I hear businesses are not doing Data Governance is because of the cost. Let me say plain and simply that statement is just not true, Data Governance can be implemented in phases and the ROI can been seen much quicker as long as you have Executive by in, do not under estimate the total amount of work, do not get bogged down with meetings and process discussion without any hands on work. Lastly make sure you understand that within a business definitions of terms may vary based on what department is defining the business term.

    With so many business are under competitive pressure and the desire for financial transparency and regulatory compliancy with HIPPA and Sarbanes-Oxley you would think those business would drive Data Governance into their culture.

    Below are some things that businesses will not be able to do without Data Governance:

  • Trust. Data Governance can give the business more trust in the data they are collecting and then what the data is telling the business. Be transparent on why and how you are collecting customer information, sharing that information and especially on how your are protecting that information
  • Identify areas that can be automated. You can reduce manual effort or completely get rid of time consuming processes that are done by your employees. A lot companies do not have an idea that there are employees spending a lot of time manual working with data and not spending their days doing what the actually were hired for.
  • Use data to retain more of their customers. Information from a acquisition and retention study existing customers are 51% more likely to try new products and spend 31% more than new customers.
  • Create consistency and make better decisions through out the business. When different parts of the business are using different data sets problems will ensue into the business. Data Governance can enforce a process where all employees are using the same consistent information
  • Strengthen your ROI. The impact Data Governance can have on a business’ ROI is tremendous for all the above reasons and more. When you apply Data Governance around the collecting of information on what your customers are doing what motivates you then have knowledge on what they want to buy.
  • Remember Data Governance is not about technology it is about defining, implementing and enforcing policies and processes for how information is generated, stored, maintained, and used across the entire business.

    Quick Tips to optimize performance using Salesforce Bulk API

    When you are using Salesforce Bulk API to load hundreds of thousands to millions of rows into your Salesforce org there are times when you will see your loading performance impacted. Below are just some quick tips for you to help optimize those data loads and integrations that are critical to your company’s success. Below are the tips that will give you the biggest bang for your buck to increase performance.

    Lets first go over quickly what is Salesforce Bulk API. The Bulk API is an interface that is based on REST principals and is optimized for inserting, updating, and deleting large sets of data in your Salesforce Org.

    One of the biggest issues that I have encountered when loading extremely large data sets using the Bulk API into Salesforce is performance (Locks and Lock Exceptions) and what can be done to increase performance based on how a Salesforce Org is configured (I always say Salesforce Orgs are like snowflakes everyone is different).

    Below are the biggest areas that you can check to get the best performance of your data loads if they are not running optimal:

    Master Detail relationships: When your Salesforce object has a master detail relationship with another object, Salesforce will lock the master record to ensure referential integrity when you are upserting a detail record. To increase performance on your loading sort your data by those Master Detail columns on that object so you can minimize how often the detail records are being inserted across your batches.

    Rollup Summary Fields: One of the biggest performance gains you can get here is remove any Rollup Summary Fields that you do not need. Salesforce will lock master records so it can update the rollup summary fields values based on the master detail relationship of the object.

    Lookup Relationships: When you upsert or delete an object that have certain type of lookup relationship to another Salesforce object, Salesforce will lock the target lookup records for referential integrity. For some performance gains you can sort by those types of lookups so you can decrease the risk of lock exceptions

    Triggers: Salesforce triggers can cause many different issues with your data loads and/or integrations. Salesforce will lock records when you are upserting or deleting a object that has one or more triggers that are selecting or updating on the other record you are performing the action on. Some thing you can try is disabling the trigger when loading your data or when your integration is running.

    Workflow Rules: Salesforce locks records when workflows rules get triggered on field updates. To get some performance increase try setting your workflow rules to not get fired off when data loads or integrations are running.

    Redshift versus Snowflake

    We are going to go over a couple of major areas to inform you whether Redshift or Snowflake is a better data warehouse for your business is based on three categories security, performance, pricing needs.

    How do these two cloud data warehouse compare to one another.

  • Deep discounts when your commitment is for a longer term
  • More unified offer package
  • Security and compliance enforced in a thorough manner for all users
  • Machine learning engine can be easily attached to
  • Little more hands-on maintenance
  • Snowflake:

  • Pay separately for compute and storage
  • More robust support for JSON-based functions
  • Tier-based packages
  • Security and compliance options will be different by tier
  • Unique architecture designed to scale on the web
  • More automated database maintenance features
  • Redshift is a solid cost-efficient solution for enterprise-level implementations. Snowflake is a good warehouse to start and grow with. If your business has less experience resources then Snowflake might be a good start for your business, where as if you have experience resources in this are Redshift would be a great warehouse for your company.

    Security: Choose your warehouse wisely
    While Redshift addresses security and compliance in a very thorough manner, Snowflake takes a more subtlety approach.

    Redshift’s encryption from start to finish can be tailored to fit anyone’s security requirements. Redshift can also be isolated within the network by being placed in a virtual private could (VPC) and then linked to an existing infrastructure (VPN). Another nice feature that can help your businesses to meet their compliance requirements with Auditing is integrating Redshift with AWS CloudTrail. The wealth of logs and analytics that you can receive will help you in the long run as far as debugging issues and shed light on performance issues.

    Snowflake handles end-to-end encryption automatically encrypting the data in transport and at rest. You are able to isolate your Snowflake with options VPC/VPN. A big difference in security and compliance from Snowflake to Redshift is that options for this grows stronger on which edition of Snowflake you opt for. This is where you have to carefully consider with edition of Snowflake will cover your needs.

    Performance: New Redshift features compete with Snowflake

    Snowflake and Redshift both utilize columnar storage (this is where data is stored by columns and not by rows) and parallel processing (this is computing that separate parts of the overall tasks are broken up) for simultaneous processing, which will save your analytical team a lot of time when processing very large jobs.

    Snowflake articulates that its performance is driven by its architecture that supports structured and semi-structured data. It places the storage, compute and cloud services separately to optimize their independent performance.

    Both Redshift and Snowflake offer concurrency-scaling (adds and removes computational capacity to handle ever-changing demand) features and machine learning to really add value to their warehouses. Both warehouses also offer free trails to their products to help companies experience their solutions value first hand.

    Pricing: Don’t stop at the sticker price but also consider long-term benefits
    Both warehouses offer on-demand pricing, but bundle associated features differently to really separate themselves from one or the other.

    The differences

  • Snowflake separates compute usage from storage in their pricing structures.
  • AWS Redshift offers users a dedicated daily amount of concurrency scaling and once usage is exceeded Redshift charges by the second.
  • Snowflake automatically includes concurrency scaling.
  • Redshift gloats the potential for deep discounts over the long term if you commit to a one or three year contract. Redshift does offer a option to pay and hourly rate.
  • Snowflake offers five options with additional features tied to each increasing the level of price.
  • When you are trying to make your final decision on which of the two warehouse to go with make sure you look at what you need specifically data volume, processing power and analytically requirements. Look for the right warehouse that will improve your accuracy and speed of data-driven decisions. Also you need to look at the resources that you have inside your business to ensure that you will be able to support the warehouse that you choose.

    Which warehouse makes sense for your business?

    Below are some additional comparisons to help guide you to picking the right solution.

  • Security: Redshift includes a deep bench of encryption solutions, but Snowflake provides security and compliance features oriented to which of the five options you choose.
  • Bundled features: Redshift bundles compute and storage to provide the immediate solution that can scale to an enterprise level data warehouse. Snowflake provides a business the flexibility to purchase only the features they need while giving the capability to scale later.
  • JSON: Both warehouse store JSON but Snowflake JSON support is a little bit stronger then Redshift. When you load JSON into Redshift you can use their build in functions but there are limitations where as Snowflake you can store and query JSON natively.
  • Mindful Data Governance Initiative

    Mindful Data Governance Initiative

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

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

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

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

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

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

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


    Thinking of your data as a Strategic Asset will help Data Governance and your business

    To implement Mindful Data Governance and have it become part of your business’s culture, the mindset of everyone in the business must believe that the data that your business is collecting is not just an asset but a strategic asset. Changing this mindset I think is extremely important and making sure the entire business understands and believes in the importance of collecting high quality data. Collecting poor quality data effects so many things within the business such as financial decisions being made by the business, marketing messages going out to customers, and which products or services are performing well and the ones that are not. These are just a very small sample of areas that will be effected by poor data quality, remember GARBAGE IN, GARBAGE OUT.

    A huge reason Data Governance should be part of your business culture is because the strategic data that is being collected does not format itself and cannot automatically identify where it is at. To satisfy the four major reasons of data governance availability, usability, integrity, and security the business must think of it’s data as a strategic asset and that way of thinking will have a huge impact not only on the success of implementing of your Mindful Data Governance Initiative but sustaining the data governance as it becomes part of your business culture. Data Governance is never finished because new sources, uses, and regulations about data are never finished. With self service bi getting more and more popular within businesses, the important of data governance as being part of your business culture will ensure users of self service BI will see the right data in the right way, to generate business insights correctly and make sound business decisions.

    Remember data is a strategic asset and thinking this way the idea of data governance becomes natural.

    Benefits of Data Governance

    Benefits of Data Governance

    There are many benefits of Data Governance and I 100 percent believe it is a requirement in todays business world not just a nice to have. I have been asked by business owners as well as C Level executives can they afford to implement Data Governance in their business? My response back to them is can you afford not to do implement data governance in your business? Cost is almost always the reason businesses do not implement data governance and that is why I developed the Mindful Data Governance Initiative, to get them over that first hurdle of worrying about cost. Alright lets jump into the benefits of data governance.

    Benefits of Data Governance

  • Improved data understanding. It really gets the entire business on the same page with the data. Data governance provides a consistent view of, and common terminology for, data, while each department retains the appropriate flexibility.
  • Better decision making. Another enormous benefit of data governance is better decision-making. This applies to both the decision-making process, as well as the decisions themselves. Data governance is more discoverable, making it easier for the entire business to find useful insights. It also means decisions will be based on the better quality data, ensuring greater accuracy and trust.
  • Regulatory Compliancy. This is another huge reason to implement the Mindful Data Governance Initiative in your business, there are hefty fines related to not following the guidelines and specifications around business process and data within different industries.
  • Huge improvement on data quality. As data governance helps in discoverability, businesses with effective data governance initiatives also benefit from improved data quality. Although technically two separate initiatives, some of their goals overlap. Data quality wants to know how useful and complete data is, whereas data governance wants to know where the data is and who is responsible for it.
  • Gain revenue. Driving revenue up happens because of all the benefits that are above. All the benefits above all the business to be more data driven, have better data quality, and allow the business to treat their data to what it actually is an asset of the business.
  • To be truly a DATA DRIVEN business DATA GOVERNANCE is a MUST