Oz Analytics

Leadership Lessons in Data Quality - Part 2

In my last blog (Leadership Lessons in Data Quality - Part 1) I talked about some useful techniques that I have learned by delivering improved data quality in a number of Australian organisations. In this post I want to talk about how to help business people to care more about DQ.

This is vital because it is the key to making good data quality sustainable in your organisation.

The short answer - like a lot of analytic challenges faced in the real world - is to measure the problem. 

Once you can measure data quality, as I mentioned in my earlier post:  I also recommend that data quality improvement of the business owners data be made a part of their performance incentive program. In simpler language: link it to their bonus.

Data quality has to be measured like any other KPI so that non-experts can understand two things:

1. What we all mean by poor data quality - define it
2. The cost of poor data quality - quantify it.

This is easiest to see in an example:

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Don't Set as a KPI:

Currently 6,753 customer records, or 2.0834% of all main customer records in EDW2 contain errors. Our objective is to reduce this by 50% within 6 months.

Do Set as a KPI:

Currently almost 7,000 clients have incorrect data that results an average of 2,800 direct mailings being returned by Australia Post. This adds $26,000 directly (printing and posting) and indirectly (administration) to each marketing campaign. Our objective is to reduce this average cost by 50% within 6 months.

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A useful technique I have developed to help me understand data and quality is to create a taxonomy that classifies data in the following three ways:

• 'Classic' data quality criteria. Things like accuracy, relevance, availability, etc. 
• The impact data quality has on the business. Things like dollars lost if data is wrong, the value of increased sales if data is correct, etc.
• What action(s) they initiate in an organisation when the data is wrong. Things like intervening at the source of data entry, remedial actions after data entry, etc.

So what metrics can you associate with data quality in your organisation? Here are six I use:

1. Accuracy: the degree of confidence that data is free of error/defect
2. Completeness: the extent to which data is not missing and is of sufficient breadth and depth for the task at hand
3. Consistency: the degree to which common data across different sources follows the same definitions, codes and formats
4. Timeliness: the degree to which data is up to date
5. Security: the degree to which data confidentiality, integrity and availability has been maintained
6. Fit for Purpose: the degree to which data is relevant, appropriate and meets business specifications.

10 Signs That You Need Analytics

I live in a world where the majority of my clients have Management Information Systems (MIS) - but not Analytics. The difference? Well, MIS is (my definition):

The people, processes and technologies used by managers of an organisation to answer their questions. Management information systems differ from regular information systems because they are for analysing other information systems used in operating the organisation.

Analytics is a specialised form (or subset of) MIS that I define as:

How an organisation uses modeling (involving extensive computation) to arrive at an optimal or realistic decision based on existing data.

In other words, MIS and Analytics are both intended to support decision making and the difference between the two is that Analytics makes use of more advanced techniques to interpret data.

The above are my practical definitions that I use when creating an analytic capability in an organisation. I can already hear the shouts and objections from my former Gartner analyst colleagues and I leave further academic debate to them. These definitions work for me and maybe you if you want.

I also see that statistical analysis and data mining are closely related - but often those activities do not involve modeling to the same degree. As to Business Intelligence I see it as an umbrella term for analytics and technologies such as reporting, OLAP, data mining, business performance management, decision performance management, benchmarking, scorecarding, dashboarding, text mining, and predictive analytics.

Now that we have that out of the way, here are the top 10 signs that you need to improve your organisation's analytic capability:

1. You have to wait longer than a day for either IT or your business intelligence department to make/change a report for you.
2. Across the organisation there are more than 100 requests pending for reporting /dashboard /scorecard changes waiting for a specialist to deliver them.  
3. When you attend meetings, there are multiple numbers being quoted for the same thing - and you don't know which of them is correct. 
4. When you talk about fundamental things like transaction, account, balance or available stock - and you discover that the person you are talking to is using the same words but means something different to what you mean. 
5. You can't get an instantly understanding when glancing at a report/dashboard/scorecard and what it is telling you. 
6. The commentary is larger than the automatically generated report. 
7. The report is not generated automatically but is a handcrafted labour of love by either yourself or one of your staff, or you spend hours trying to locate the right data and then have to consolidate it manually into Excel. 
8. It takes longer than 5 minutes to view a new report. 
9. You can't access the report when and where you most need it. 
10. There are hundreds of reports available to you but you don't trust them and you spend time trying to manually validate key numbers.  

If these are the signs, then what are the solutions? That, dear readers, I leave to another blog entry ...