Archive for the ‘Management’ Category

We have all been in the situation where applications/software we implement gets shot at because of the poor data quality which gets shown to the end users through those applications. In most situations it is really easy to shoot the messenger (Application/software in this case) rather than address the root cause behind the message (poor data quality). Time in and time again we have experienced shooting of the messenger by everyone in the business (because it is easy to do but really not good for the outcome it brings about for the business). In this discussion, I am going to talk about “what to do to avoid getting into trap of data quality issues when BI projects/initiatives are undertaken?”

BI (I am using BI or Business Intelligence as term in a broad sense to represent all reporting, data warehousing/datamarting or analytics initiatives in the organization) is often a catalyst in bringing data /process quality issues to forefront really fast and easily. Given the visibility and pervasive value BI brings to the organizational informational needs, in cases when data quality issues are brought to forefront, suddenly debate ensues if the focus should solely be shifted on fixing data quality issues and putting BI initiatives on back burner until data quality issues are fixed. This approach/organizational attitude(I have experienced this more so in SMB customers) has some risks/issues associated with it (as mentioned below), and BI teams should avoid this trap by being proactive.

1. Belief that data quality can be completely fixed, creates unreasonable expectations and sets the whole team (which is likely to work on fixing data quality) up for failure. Data quality issues will exist as long as data is created and used by people. Citing data quality as a reason to defer BI implementation will deprive organization of the Informational needs which could be met by BI. Remediation of data quality is an iterative process (and not a one shot deal) and this is discussed in his recent post by Jim Harris in “Missed It By That Much

2. BI initiatives will always highlight possible data quality and process issues, making data quality as one of the major contingencies to derive value from BI initiatives is short sightedness (it is not as black and white as it is made to sound by people looking at what is wrong because of data quality issues)  and will not help organizations derive proper ROI and edge from the BI investments.

So what do you do when data quality issues threaten to derail the BI initiatives?

Proactive: BI initiatives are driven by organizations for the insight they offer into the data for making operational as well as strategic decisions. Because BI initiatives make it very easy to look at both aggregated and detail level data across the organization, they often highlight many data quality and process related issues within the business. It is really important to highlight this side benefit of BI implementation to all stakeholders’ right from beginning and all throughout BI initiative:

1. BI initiatives will make it very easy to spot data quality issues, use artifacts from BI initiatives as your microscope to find data quality issues.
2. They will highlight process issues by showing gaps or lack of relationships in data
3. BI initiatives can be and should be used to benchmark and monitor ongoing data quality and enforcement of business processes.

In fact, including these benefits of BI into the ROI calculation will ensure that

1. When data quality issues are found, organization will not be surprised. It’s almost like they are expecting BI initiative to highlight Data/Process quality issues.
2. BI initiatives will get credit (towards ROI) when these data quality issues are found and monitored.
3. Artifacts from BI (Like reporting or data warehouse, data mart, Single view of data) will now have official role to play in overall data quality measurement and monitoring imitative

What can BI teams do proactively to address data quality issues before they snow ball into show stoppers for BI initiatives?

Process for Identifying Data Quality Issues before BI Implementations

Process for Identifying Data Quality Issues before BI Implementations

1. Prioritize functional/business areas for which BI is to be implemented, if this is already done, stick with existing priorities.
2. Distill down those priorities in to set of business questions(10-20 for each requirement to cover all bases) which BI initiatives will answer. These questions will have to be developed in partnerships with business users

For example: After implementing data mart for pipeline analysis, marketing department should be able to do competitive analysis to understand which competitor is being run into most of the time. Allow trending and analysis of this information by all aspects of the pipeline (Time, Sales Organizations, Regions, Verticals etc…)

3. Itemize data which will be required to answer/fulfill business question. For example in above scenario following data should be available.

a. Pipeline/opportunity records data with competitor names captured
b. Opportunity data where sales organization is captured on it etc….

4. Now that you are aware of the data groups required to support the business question, do profiling of those data groups: In above example, you want to look at competitor field, profile that field in light of the question and understand

a. Density: How often is competitor and win/loss field captured? (e.g. only 10%)
b. Accuracy/Validity: How many instances of competitor field are usable Vs useless? (Out of 10% from the above, 70% are usable)
c. Enrichment effort: How many instance of competitor field needs cleaning? (Out of 70% from above, we need to clean up almost 50%)
d. Recency: What is the trend (recency) of entering this information (May be sales started entering this data recently, for last two quarters you had 80% of transactions with this information, so if focus is for last two quarters, reasonable amount of information is available for answering question above)

5. Once equipped with this information build the scorecard for accuracy of the data and validity of information/insight which can be gleaned from this data in the context of question…

6. So to come down to making decision about what BI team should do next, one will need to build necessary context around accuracy of information Vs. observed data quality to provide proper idea about what business users can expect to get out of this initiative if continued as is….In above example, some of the metrics which can be defined could be

i. If all of available data is looked at for analyzing competitive information only 10% of data (at best) would have any information. So if you do not put any constraint on the time frame, you are going to get insight into only 10% worth of business at best. This will require some effort on the part of parties involved to clean the data; otherwise this percentage will go further down.
ii. If you look at only last two quarters and information going forward, best you can get visibility is about 80% (provided data quality/cleanliness issues are addressed otherwise it will be less than that) of your business.

Sample of data quality score card

Sample of data quality score card

iii. If business decides to go ahead with the option (ii) above, an agreement/plan needs to be reached to have marketing/sales department implement the process to enter competitive information on each deal (going forward) as well as BI needs to commit to implement metrics/reports/dashboard to track this as one of the indicators and provide comparison of data in percentages as well as absolute numbers (Sample shown in the data quality score card image) to show ongoing progress or lack there off.
iv. If option 2 above is not acceptable to the business users, BI initiative needs to implement metrics to track the current data quality and ongoing data quality as a first step towards continuing BI initiative. This will help in motivating marketing/sales department to make necessary investments into fixing data quality (at least going forward). This won’t totally derail the BI initiative. After implementing the quality measurement initiative, BI team can still go ahead and implement the rest of the solution and engage all parties to start working on data quality (where ever applicable). This way when data quality goals are achieved by business or better understood by business they can start using BI implementation for competitive analysis with proper context of data quality in mind (I can use all of the data if I am doing competitive analysis for Printers and it will be roughly about 60% complete across all time frames, but for other products, I am better off looking at YTD data)

Doing above exercise will help in avoiding any surprises resulting out of visibility into data quality issues during BI implementations. This will also help in positioning BI as critical and required partner in enterprise data quality initiative. With some efforts, planning (and a little bit of luck), you can both save the messenger and also will allow business to attack the message.

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During tough economic times, CFOs are tightening the belt and are looking into managing expenses across organization; in order to achieve necessary cuts in the expenses they recruit help from all business units/division/areas within the organization. Managing expenses is enterprise wide tasks and CFOs (Chief Financial Officers) lead this initiative. Impact of managing expense is far reaching; it helps organization to impact profitability by reducing expenses.

I believe that data quality is an enterprise level challenge as well; data quality affects all the parties who use, generate or maintain data for the purpose of managing their business. Typically CIOs (Chief Information Officers) lead (or should lead) all initiatives associated with data quality and they should look for recruiting help from across the organizations (just as CFOs or CEOs do). Creating enterprise wide awareness and urgency about the impact of the issues associated with data quality (and hence information quality) will ensure success and proper level of sponsorship of the data quality initiatives within the organization.

While there are several ways in which impact of the data quality can be highlighted to the organization, following are some practical examples which I have found to be easy to use while communicating soft ROI or impact of data quality on individual business units.

Sales Organization:

Capturing good quality data would directly impact on helping to close additional deals or help with closing of the deals which might have been lost otherwise. While this is a broader statement, I can give few examples where this has helped me in realistic situations….

Capturing Win/Loss reasons consistently on deals sales team marks as closed (Won or Loss) would result in better analysis of reasons why organization is losing deals. Based on this information, sales and sales operations organization can always recruit help from marketing or engineering to overcome reasons behind the losing those deals. Many times once better understanding about why deals are being lost against a competitor or lost in industry vertical is well established, minor tweaks in positioning or minor product enhancements could turn the tide and will reduce loss of deals in those situations. Of course all this will be possible if good quality data about win/loss reasons is captured. In order to make sure that this data is captured on an ongoing basis, Sales Operations need to commit to investing time on monitoring capture of Win/Lost reasons. They also need to figure out if there are standardized templates they might want to use to capture this information succinctly.

Another example in similar category would be to capture the competitor information (Competitor name, name of the products being competed against) on all deals. Better analysis of the win/loss data against competitor can help with recruiting marketing/engineering help to fend off competition. Again what it takes is qualitative data about competition and products in every deal in which sales is competing.

Interesting thing about this is that one or two deals (off-course depending upon your ASP) salvaged could pay for entire incremental expense of enterprise data quality initiative for the year or so.


Amongst many aspects, marketing department budgets and effectiveness is decided by how many sales qualified leads are generated on periodic basis. In many instances, speed with this Marketing is able to nurture and grow leads to be sales qualified leads really depends on the quality of the information captured. These days marketing automation tools provide personalized messaging but for that messaging to work marketing really needs to gather quality information about leads. It’s relatively easy to justify effectiveness of marketing campaign or even lead nurturing process by considering data quality of the leads data. For example, a client of mine decided to market their product to clients who had ERP system installed in-house (SAP or Oracle Applications). Unfortunately, lead data which was captured through webinars and trade shows did not capture this information in a consistent format; on top of that this field had a lot of data quality issues (free form text). Investing marketing and IT time to clean this data would greatly increase the effectiveness of any campaign or lead nurturing program to be run against these leads.

Operations and Finance:

If finance or operations team is particular about capturing all the contract and customer/account details in a clean way first time around on the transactions, they can save time it takes to ship and then bill the products to customer (save time wasted on figuring out where to ship/where to bill etc…), thus saving several days on shipping and invoicing clients. This will directly result in improved cash flows (early billing = early countdown of terms = early payment), greater customer satisfaction etc…If the data on existing contracts or existing customer is not clean and if your organization is engaged into business of reselling into those accounts or on renewals, it is worthwhile to make an effort to ascertain data quality of this data and save several days it takes to go through shipping and billing process.

In summary, I believe that data quality is enterprise wide issue which impacts almost everyone who creates uses and maintains data generated through business operations. CIOs need to champion the data quality cause at enterprise level just the way CFOs or CEOs champion the cause of cost cutting across enterprise by enlisting help from each and every department. Some of the ideas given above can be used for highlighting softer ROI of data quality across enterprise. Details and specifics of the above examples will vary from organization to organization based on the business they are in and the business model they have within their organizations.

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Welcome to my blog. This blog I will discuss wide ranging topics from Application development, development management to BI/Analytics, Data quality, Business Processes etc…From time to time I will also update on my reading lists. I would love to get your feedback and comments from time to time so, please go ahead and make this interactive, post your comments, refer this blog into your blog….

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