Data Driven Organization: Key to Data and Process Transparency

In his recent blog titled Data and Process Transparency Jim Harris makes the case that “a more proactive approach to data quality begins with data and process transparency”. This is very true of any organization striving towards availability of highest quality data for its decision-making as well as efficiency of business processes.

In order to embed transparency in every situation across organization, organizations need to be data driven. After all, what does it mean to be data driven organization, you may ask? There is great deal of literature around this topic in print as well as on the web. I will try to simplify this discussion and say that, to me, when culture of decision making is purely based on the factual data (KPIs/Metrics etc…) within an organization (and not on gut feel, emotions and subjectivity of decision making individuals), organization can be said to have become a data driven organization. Of course, this is a very simplistic definition (just for the purpose of this blog).

Many a times, depending upon organizational maturity you may have organizations which are completely data driven versus organizations which are more mature in one area (vis-a -vis data driven decision-making) versus other areas of the organization. For example, in some cases finance side of the organization might be much more data driven than either marketing or sales site etc.

Using data to make decisions drives both data and process transparency across organization. It discourages use of anecdotal information (and gut feel) and forces people to think in terms of realistic data and evidence presented by data. Also using specific KPI’s/metrics allows organizations to clearly define issues associated with underlying data or business processes more readily.

For example, if the sales operation team is discussing order return rates, they cannot simply say that we have a very low order return rate because of poor addresses in a “data driven organization”. They will say that they have 1% order return rate for (on average) 125,000 orders they ship every month because of the poor shipping addresses. This way of expressing performance not only helps everyone involved in understanding the importance of good data quality but also helps organization with creating sensitivity around capturing good data to begin with. Also expressing performance this way helps with ready-made business case for supporting underlying data management initiatives.

Transforming organizations to a data driven organization is a gargantuan change management task. It requires significant cultural/thinking change up and down the organizational hierarchies. During such transformations, organizational operational DNA is completely changed. Obviously, the benefits and rewards of being the data driven organization are immense and worth the efforts of transformation.

On the other side, during the data driven organizational transformation if organizations find that data is not of reliable quality, this finding will force data management discussion across organization and help kick start initiatives to fix the data as more and more in the organization start using data for decision making.

In end, I would encourage everyone to be as data driven as possible in their decision making and influence areas within your organization to be data driven. As data professionals, this will allow us to be more proactive in addressing data management challenges for the organization.

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Data Governance Litmus Test: Quality, Reliability and Timeliness of the Data

This is a seventh blog entry in a blog series highlighting the criticality and the importance of executive sponsorship for data governance initiatives. In last entry we identified the need for capturing basic contextual information around systems, business processes, which has been automated by those systems and data being used by those systems to automate business processes. This information will help you in base lining current level of policies, procedures and their implementation as it is relates with systems, business processes and data. Armed with this information, you should have clear visibility into who (from the teams prospective), which (from a systems perspective), and what (from data perspective) will be involved in the prescribed scope (by KPIs) for your data governance initiative

At this stage it becomes necessary to profile the data, analyze the data for the accuracy, cleanliness and completeness. Reliability of the data from the perspective of business process which uses this data as well as the KPIs in which this data is used must also be evaluated. Profiling and analyzing data will also help you in capturing any policies and procedures which have not been explicitly defined, documented in the system of record, but yet are being implemented at data level (for example, after profiling data you might realize that only two payment terms are present in the data, but there is no explicit mention of this as a part of the policy definition)

In one of my consulting engagements with high-tech companies, I was methodically going through this process. CFO had identified reduction in DSO as one of the key goals for the year. Following these steps, when we mapped the actual systems being used to capture and report DSO, we realized that instead of being one financial application, this organization had three financial systems one which was supposed to be a corporate standard and two which had not been fully migrated as a result of past acquisitions. This was a big eye opener not only for me but all the people who have been crunching numbers to report DSO to CFO. To make the matters even complicate further, data at the summary level was being transferred from other two systems to the organizational standard financial application once in every 15 days. This meant that CFO was not getting accurate data for DSO on a week to week basis. Also, when we did some data analysis/reconciliation between these three systems, we found many data discrepancies (mainly associated with dates on transactions etc.) because one system was capturing summary level data versus other systems had detailed data.

Doing this detailed analysis of various systems and processes involved helped us (i.e. data governance team) in establishing the quality, usability, and accuracy of the data coming from three systems. It also helped us quickly identify the need to migrate two non-standard financial systems still being used to the corporate standard. The benefits of system migrations were very huge for the organization (standardization and optimization of collection processes, standard payment term usage and enforcement, being able to use appropriate cleansed addresses for billing thus reducing any delays because of wrong billing addresses, being able to leverage the data enrichments done to customer accounts in the main system, Being able to generate more accurate DSO metrics for reporting purposes etc.)

Documentation of this information in the metadata repositories and modeling tools really help application team responsible for maintenance of the financial systems in estimating and kick starting initiatives around system migrations. Also knowledge of specific data issues and possible benefits of fixing those data issues, standardizing policies (credit, collection, billing, etc. ) across the three areas of the organization helped us in demonstrating the benefits which can be had in improving DSO KPI.

By far, I would say that critical understanding of data issues, interdependency between different systems, teams (people) and business processes, and a clear understanding of lack of (which) policies or consistencies between the policies is extremely vital for the process of identifying impact of data governance initiatives on organizational strategic direction.

Previous Relevant Posts:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Data Governance Litmus Test: Know goals behind KPIs

Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

Data Governance Litmus Test: Systems, Processes and Data Behind the KPIs and Goals

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Data Governance Litmus Test: Systems, Processes and Data Behind the KPIs and Goals

This is a sixth blog entry in a blog series highlighting the critical nature and the importance of executive sponsorship for data governance initiatives. In last few entries, I explored need to understand the KPIs, goals behind those KPIs and necessity to get your hands on actual artifacts used by executives in reviewing these KPIs and their goals.

My approach has been very simple and straightforward: data governance initiatives need to absolutely be able to demonstrate impact on top and bottom lines by helping executives improve on the KPIs which are used as means to achieve higher profitability, lower costs and compliance. The process of garnering executive sponsorship is a continuous one. Visibility of data governance organization, its impact across the board; helps in establishing awareness and understanding of how data governance initiatives help organizations. This visibility and awareness makes it easy to maintain ongoing executive sponsorship.

Once you, as a data governance team, have clearly understood KPIs, goals behind those KPIs and have access to the artifacts used by executives, it is time to go back to the technical details. At this stage it is extremely important to map which systems, business processes automated by those systems and data is either directly or indirectly responsible for the outcome of those KPIs. This process of mapping dependency between KPIs, systems, business processes and data can be somewhat automated using metadata management repositories. It is important to capture this information using tools and technologies so that this information can be readily available and shared with other teams and systems.  Technology solution will also facilitate change management, impact analysis in future. The lineage and the metadata I am talking about here, go beyond technical metadata and gets into the realm of business (process and semantic) metadata as well.

This dependency information will come in very handy in establishing scope, definition of the efforts being planned towards specific data governance initiative/project. When collecting information about the systems, business processes automated by those systems and data, it is important to capture relevant information with long-term, repeatable perspective. Information such as:

1.     System name and information,,

2.     Landscape information (where is it being installed/managed/housed, which hardware/software are being used? touch points with other systems etc.)

3.     Ownership and responsibility information from both business and technology perspective. (Which technology teams are responsible for managing, changing and maintaining these systems? Who are the business stake holders who approve any changes to the behavior of these systems? etc.)

4.     Change management processes and procedures concerning the systems and data.

5.     End-users/consumer information (who uses it? How do they use it? When do they use it? For what do they use it? In).

6.     Any life cycle management processes and procedures (for data, systems) which might be into existence currently.

7.     Specific business processes and functions which are being automated by the systems?

Many a times, some of this information might already be present with the teams managing these systems. This exercise should identify presence of that information and make a note of that information. The point here is not to duplicate this information. If the information does not exist, this exercise will help capture such information which is relevant not only for the data governance initiatives, but is also usable by system owners and other stakeholders.

Goal of this step/answering this question is to baseline information about systems, business processes automated by the systems and data. This information is going to help in subsequent stages for establishing, change management processes, defining policies and possibly implementing and monitoring policies around data management/governance.

From this phase/question data governance initiative starts transitioning into nuts and bolts of the IT systems and landscape. In next few blog posts, I will be covering various aspects which data governance team should consider as they start making progress towards establishing official program and start working on it.

Previous Relevant Posts:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Data Governance Litmus Test: Know goals behind KPIs

Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

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Fog and Poor Data Quality

Yesterday as I was driving to work, we had fog everywhere in the area in which I live. We where fogged in so to speak. Typical commute from my house to the nearest freeway takes about 10 minutes on a given day, yesterday, it took 25 minutes. Visibility was poor; I could hardly see more than 100 yard ahead of me and about the same distance behind me. This meant I was driving very cautiously; not at all confident about what was ahead of me. While I was driving through this dense fog a thought came to my mind, isn’t it true that business decision makers go through similar predicament when they are faced with an lack of availability of reliable, high quality data for decision-making?

Poor data quality means lesser visibility into performance of the organization; it also implies impairment of decision-making based on actual data. As with a fog, poor data quality means business decisions are done slowly, over cautiously and many a times based on gut feel, rather than factual data. Slowness in decision-making could mean possible loss of the edge business has over its competition. I feel that there is a lot common between driving through a fog and trying to run the business with poor quality data.

As sun rises and temperature increases, fog will burn out. In the same way effective data quality and data governance initiatives will help burn away the fog created by a lackluster data quality. Burning off the fog is a slow and steady process; all the right conditions need to exist before fog disappears. It is the same with addressing data quality holistically within enterprise. Right conditions need to be created in terms of executive sponsorship, understanding of importance of good data quality, clear demonstration of value created by data assets etc. before true fruits of data quality initiatives can be harvested.

Superior data quality and timeliness of availability of high-quality data has significant impact on day to day business operations as well as strategic initiatives business undertakes.

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Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

This is a fifth blog entry in a series of blog entries highlighting how to go about securing executive sponsorship for data governance initiatives? In my last post I highlighted the need for understanding the KPIs which are tracked by executives and the importance of clear and very specific knowledge of the goals behind those KPIs.

As you might have already noticed, these steps one goes through to answers litmus test questions, helps data governance organization with establishing a direct relationship between data governance initiatives and organizational priorities. Getting executive sponsorship is not a one shot deal. It is an ongoing process which needs to be initiated, maintained throughout the lifecycle of data governance initiatives.

It is important to get actual copies of the reports/presentations/summaries which executives use to review the progress of the key KPIs in executive management meetings. This will help data governance team in multiple ways.

1. You will have very clear understanding of how the information provided by KPIs is consumed by executive management? Who is looking at this information and what frequency?
2. The process of getting these copies will get you access to executives or people around executives who can give you access to executives. This is extremely important as data governance programs seek executive sponsorship.
3. Making executives and people around them aware that data governance team is a critical recipient of the artifacts which are being used by executives, so that in future should any KPIs, goals, expectations, change executives/ executive office will notify data governance team. This way allowing you to establish data governance team as part (or recipient) of the priority/goal change management process.
4. These artifacts will help you understand individual executives’ styles around data presentation, consumption. This will be of immense help to you, when you present the data governance ROI and case to the executives.
5. Periodic copies of these artifacts will help you in establishing baseline for the KPIs and use this baseline to report progress around data governance initiatives.

As I write about these 10 questions for the litmus test of data governance initiatives to evaluate level and extent of executive sponsorship to the data governance programs, my approach has been to use these questions to help create a journey for data governance team which ultimately will help the team in garnering executive/business sponsorship. As you can see, working on getting answer to these questions will create necessary awareness, visibility amongst executives and business stakeholders. So when the time comes secure executive sponsorship it is not a surprise to the key people who will be asked for their support.

Previous posts on this or related topics:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Data Governance Litmus Test: Know goals behind KPIs

Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

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My Experience with Good Enterprise Information Management (EIM) Practices in Play

This post was encouraged by similar writing about good data management post by fellow practitioner and blogger Henrik Liliendahl (Right the First time)

For data professionals like me, who have been working and preaching importance of the data quality/cleanliness and data management, it feels really good when you see good examples of enterprise information management policies and procedures at play in real life. It feels like the message of importance of data as an asset, as a “critical success enabler for the business” is being finally heard and accepted.

Recently I had a wonderful experience shopping for a laptop at http://www.dell.com. As I was shopping on their website, I configured my laptop (I’m sure all the options for my laptop were being populated from a Products MDM catalogJ). When I was ready to check out, before calculating shipping charges, website prompted me to enter my shipping address. When I entered my address, website came back to me with two corrected addresses which where enriched with additional information such as four digit extension of the zip code, expanded abbreviations etc. Website forced me to choose one of the corrected/enriched addresses before I proceeded with my order completion. This probably meant that they have implemented a solution which checks for validity and conformance of address information being entered before letting this information enter into their systems. Obviously, this investment from Dell has many benefits for Dell and hence they must have invested this effort in implementing these data quality/standardization solutions as a part of broader Enterprise Information Management framework. I was really happy for Dell. This process also meant that my order was delivered ahead of schedule without additional charge.

I am writing this because I believe in applauding and appreciating efforts done the right way. For transparency purpose, I am not related with dell.com in any professional way (employment, contract etc…), also nor did dell hire me to write this blog post. I am one of the thousands of customers they have. I just want to say good job Dell.com

I would like to appeal to all fellow bloggers and practitioners to cite examples of good information management, data management or data governance practices at work from public domain and write about them. Tweet about them under #goodeim tag. We have heard too many horror stories; there are many organizations which have been diligently at work implementing very successful information management practices, let us encourage and applaud those efforts openly.

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Data Governance Litmus Test: How and who is putting together metrics/KPIs for executives?

This is a fourth blog entry in a series of blog entries highlighting how to go about securing executive sponsorship for data governance initiatives. In previous posts, I have highlighted the need for  understanding specific KPIs/metrics which executives track,  and tangible goals which are being set against those KPIs.

Almost always, there is either individual or group of individuals who work tirelessly on producing these necessary reports with KPIs/metrics for executives. Many a times these individuals have clear and precise understanding of how these metrics/KPIs are calculated, what data issues, if any, exists in underlying data which supports these metrics.

It is worthwhile to spend time with these groups of people to get a leg up on an understanding of metrics/KPI definitions, knowledge around data issues (data quality, consistency, system of record). The process of engaging these individuals will also help in winning confidence of the people who know the actual details around KPI/metrics, processes associated with calculating and reporting on these metrics. These individuals will likely to be part of your data governance team and are crucial players in winning the vote of confidence from executives as it relates to the value data governance initiatives create.

In one of my engagements with a B2 B customer, executive management had the goal of improving business with existing customers. Hence executive management wanted to track Net new versus Repeat business. Initially sales operations team had no way of reporting on this KPI, so in the early days they reported using statistical sampling. Ultimately, they created a field in their CRM system to capture new or repeat business on their opportunity data. This field was used for doing new versus repeat business KPI reporting. Unfortunately, this field was always entered manually by a sales rep while creating opportunity record. While sales operation team knew that this is not entirely accurate, they had no way of getting around it.

In my early discussions with sales operations team, when I came to know about this, I did a quick assessment for a quarter worth of data. After doing basic de-duping and some cleansing I compared my numbers versus their numbers and there was a significant difference between both of our numbers. This really helped me get sales operations team on board with data cleansing and ultimately data governance around opportunity, customers and prospects data. This discussion/interaction also helped us clearly define what business should be considered Net new and Repeat business.

Obviously, as one goes through this process of collecting information around metrics, underlying data and the process by which these numbers are crunched, it helps to have proper tools and technology in place to capture this knowledge. For example

a)     Capturing definition of metrics

b)     Capturing metadata around data sources

c)      Lineage, actual calculations behind metrics etc.

This process of capturing definitions, metadata, lineage etc. will help in getting high level visibility of the scope of things to come. Metadata and lineage can be used to identify business processes and systems which are impacting KPIs.

In summary, this process of finding people behind the operations of putting together KPIs helps in identifying subject matter experts who can give you clear and high-value pointers around “areas” which data governance initiatives need to focus early on in the process. This process will ultimately help you in recruiting people with right skill set and knowledge in your cross functional data governance team.

Previous posts on this or related topics:

Litmus Test for Data Governance Initiatives: What do you need to do to garner executive sponsorship?

Data Governance Litmus Test: Know thy KPIs

Data Governance Litmus Test: Know goals behind KPIs

Suggested next posts:

Data Governance Litmus Test: Do You Have Access to the Artifacts Used by Executives?

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