Business Framework for Analytics Implementation

14 Sep

Updated 9/14/20 with new links. It is a bit ironic that I linked to the Dataversity site, and they do not use persistent identifiers to label their data assets, so all my links are dead. Note to practitioners – if you are not using persistent identifiers your institutional knowledge captured in data assets lasts as long as the identifier!

I went looking for this deck as I was having a discussion on governance that is as old as the hills; essentially how do you link data governance activities to the business activity to address – why does data governance exist?

The other discussion that got me looking at this article again was how we go about building an operating model for organizations where the Governance team is doing more than responding to quality requests – how does the team proactively address data issues?

Both of these are tied to the article below. The Hoshin Framework (at least as it is presented below) ties strategic initiatives all the way down to identified data capabilities that can be addressed proactively to support the business strategy. 

A note on the spreadsheet. This spreadsheet is not for the faint of heart. The spreadsheet supports the thought exercise used to shape discussions and your communication with stakeholders. The key point to take away is that the spreadsheet gives you the ability to relate governance budget to strategic goals, funded programs, current project and metrics. Think of it as the audit worksheets – no one ever sees those, and the auditor reports out only the results.

Original Post.

In my previous post I discussed some analytical phrases that are gaining traction. Related to that I have had a number of requests for the deck that I presented at the Enterprise Dataversity  – Data Strategy & Analytics Forum.  I have attached the presentation here. NOTE: This presentation was done a few years ago while I was with CMMI (Now ISACA) as a result it is tied to their Data Management Maturity Model. I talked about analytics, and my colleague on the presentation addressed data maturity.

Also, while I am posting useful things that people keep asking for, here are a set of links that Jeff Gentry did on management frameworks for a Dataversity Webinar. Of particular interest to me was the mapping of the Hoshin Strategic Planning Framework to the CMMI Data Management Maturity Framework. The last link is the actual excel spreadsheet template.


  1. Webinar Recording: Here is link to deck.
  2. Link to Using Hoshin Frameworks. Hoshin is bigger than just this matrix, and is a heavy process for most people. However, the following gives you soem background:
  3. Hoshin Framework linked to DMM: Data Analytics Strategy and Roadmap Template 20160204D.xlsx

What am I doing…?

17 Nov

Someone asked the other day if I was still blogging – the answer is yes, but… During the summer I joined the teaching team at University of Maryland to teach a course in data governance and data quality in the Graduate Information Management Program. Between creating the course content and teaching the course, it has consumed all of the creative energy that I normally put into the blog articles.

Hang tight. I will be posting again soon.

Data Prep – More than a Buzzword?

25 Feb

“Data Prep” has become a popular phrase over the last year or so – why? At a practical level, data preparation tools are providing the same functionality that traditional ETL (extract, transform, load) tools provide. Are data prep tools just a marketing gimmick to get organizations to buy more ETL software? This blog seeks to address why data prep capabilities have become a topic of conversation within the data and analytics communities.

Traditionally, data prep has been viewed as slow and laborious, often associated with linear, rigid methodologies. Recently, however, data prep has become synonymous with data agility. It is a set of capabilities that pushes the boundaries of who has access to data, and how they can apply it to business challenges. Looked at this way, data prep is a foundational capability for digital transformation, which I define as the ability of companies to evolve in an agile fashion in some key dimension of their business model. The business driver of most transformation programs is to fundamentally change key business performance metrics, such as revenue, margins, or market share. Viewed in this way, data prep tools are a critical addition to the toolbox when it comes to driving key business metrics.

Consider the way that data usage has evolved, and the role that data prep capabilities are playing.

Analytics is maturing. Analytics is not a new idea. However, for years it was a function relegated to Operations Research (OR) folks and statisticians. This is no longer the case. As BI and reporting tools grew more powerful and increasingly enabled self service for end users, users began asking questions that were more analytical in nature.

Data-Driven decisions require data “in context.” Decision-making and the process that supports it require data to be evaluated in the context of the business or operational challenge at hand. How management perceives an issue will drive what data is collected and how it is analyzed. In the 1950’s and 1960’s, operations research drove analytics, and the key performance indicators were well established. These included time in process, mean time to failure, yield and throughput. All of these were well understood and largely prescriptive. Fast forward to now. Analytics is broadly applied and used well beyond the scope of operations research. New types of analysis driven in large part by social media trends are much less prescriptive and value is driven by context. Examples include: key opinion leader, fraud networks, perceptual mapping, and sentiment analysis.

Big data is driving the adoption of machine learning. Machine learning requires the integration of domain expertise with the data in order to expose “features” within the data that enhance the effectiveness of machine learning algorithms. The activity that identifies and organizes these features is called “feature engineering.” Many data scientists would not equate “data preparation” with feature engineering, yet there is a strong correlation to what an analyst does. A business analyst invariably creates features as they prepare their data for analysis: 1) observations are placed on a time line; 2) revenue is totaled by quarters and year; 3) customers are organized by location, by cumulative spend, and so on. Data Prep in this context is the organization of data around domain expertise, and is a critical input to the harnessing of big data through automation.

Data science is evolving and data engineering is now a thing. Data engineering focuses on how to apply and scale the insights from data science into an operational context. It’s one thing for a data scientist to spend time organizing data for modest initiatives or limited analysis, but for scaled up operational activities involving business analysts, marketers and operational staff, data prep must be a capability that is available to staff with a more generalized skill set. Data engineering supports building capabilities that enable users to access, prepare and apply data in their day-to-day lives.

“Data Prep” in the context of the above is enabling a broader community of data citizens to discover, access, organize and integrate data into these diverse scenarios. This broad access to data using tools that organize and visualize is a critical success factor for organizations seeking the business benefits of digitally enabling their organization. Future blogs will drill down on each of the above to explore how practitioners can evolve their data prep capabilities and apply them to business challenges.

The topic of protecting personal information will grow in importance in 2019

19 Nov
IAPP Annual Report 2018
For those interested in the protection of personal information, the IAPP has an interesting – albeit rather hefty – IAPP-EY Annual Privacy Governance Report 2018, and the NTIA has released its comments from industry on pending privacy regulation. I noted that the IAPP report indicates most solutions are still almost all or entirely manual. I am not sure how this does not become a management nightmare as organizations evolve their data maturity to align operations and marketing more. Data management as a process discipline and some degree of automation are going to be critical capabilities to ensure personal information is protected. There are simply too many opportunities for error when this is done manually. 
I recently published an article in TDAN on automating data management and governance through machine learning. It is not just about ML, other capabilities will be required. However, as long as organizations rely on manual processes only, it opens up risk and places the burden on management to enforce policies that are often resisted as they are perceived as a burden on actually doing business. Data management as a process discipline in conjunction with automated processes will reduce operational overhead and risk.

Architecting the Framework for Compliance & Risk Management

24 Oct

Really quick visit to the Data Architecture Summit this year. I wish I could have stayed longer, but I had to get back to a project.

My presentation was on creating audit defensibility that ensures practices are compliant and performed in a way that is scalable, transparent, and defensible; thus creating “Audit Resilience.” Data practitioners often struggle with viewing the world from the auditor’s perspective. This presentation focused on how to create the foundational governance framework supporting a data control model required to produce clean audit findings. These capabilities are critical in a world where due diligence and compliance with best practices are critical in addressing the impacts of security and privacy breaches.

Here is the deck. This was billed as an intermediate presentation and we had a mixed group of business folks and IT people with good questions and dialogue. I am looking forward to the next event.

Agile – we just keep trying to make it work!

3 Aug

In the summer of 2013, I must have been thinking about Agile approaches to development as I wrote two blogs on the topic:

I was interested to see that Martin Fowler released an article on yet another approach to fixing what is wrong with agile; the Agile Fluency Model. The article provides a good comprehensive write up on this approach. However, go back to look at the links in the above blogs. There are a number of amusing ones. This one from Martin Fowler titled Flaccid Scrum, and these two very amusing ones here and here.  They all refer to the same set of challenges facing how agile is implemented.

I am not sure I have anything to add to the debate. however, I do note that successful teams invariably: 1) involve a white board; 2) engage in lively and dynamic dialogue around the challenge; and 3)  have team members with an intuitive user centric understanding of the problems the team seeks to solve.

I guess I am also surprised that we are still talking about how to “do” agile!

Link to agile Fluency Model Diagnostic

Update: Interesting article here by Joshua Seckel 

DGIQ 2018

12 Jul

The DGIQ conference this year went well. I had two presentations, caught up with industry colleagues and customers. It helped that it was in San Diego – and the weather relative to the hot mugginess of the Mid Atlantic was excellent.

My presentation on GDPR was surprisingly well attended. I say surprising in that the deadline has passed, and I find that there are still companies that are formulating their  plans. However, I am beginning to feel a bit like Samuel Jackson.


In the GDPR presentation, the goal was to focus attention on not only doing the right thing to be compliant, but also doing it right. How do we reduce the stress and overhead of dealing with regulators. We call this “Audit Resilience.”  I spoke to a number of people that are taking a wait and see approach to GDPR compliance. Interestingly even though they are taking this approach, they are still getting requests to remove personal information. It seems to me that if you are taking a wait and see approach, you really still need to be able to remove personal information from at least the web site otherwise, you risk triggering a complaint, and then … you have no defense. Goal has to be to do everything not to trigger a complaint. The presentation took about 15 minutes, and the rest of the time was spent demonstrating the data control model in the DATUM governance platform – Information Value Management.

Also had the pleasure of presenting with Lynn Scott who co chairs the Healthcare Technology & Innovation practice at Polsinelli with Bill Tanenbaum – what we wanted to do was push home the point that collaboration is key when dealing with thorny risk and compliance issues. We tried to have some fun with this one.

I will be at the Data Architecture Summit in Chicago in October. The session will cover:

  • What are the requirements to ensure management is “audit resilient”?
  • What is a Control System and how is it related to a Data Control Model?
  • What is “regulatory alignment” from a data perspective?
  • How do I build a Data Control Model?
  • What role do advanced techniques (AI, Machine Learning) play in audit resilience?

Hope to see you all there

3stooges happy

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