Data Quality Business Case – A Hygiene Analogy

Constructing a sound business case to support a data cleansing / data quality program can be difficult. There are a few reasons for this and I am sure those who have attempted to get a DQ program off the ground will testify to this. There are the usual responses around data ownership, responsibility etc. But to my mind the biggest hurdle is in establishing a sound relationship between data quality and the bottom line. We can reel off the usual arguments – number of movers each year, cost of duplicate mailings, number of deceased each year etc. But somehow these start to sound a bit lame when applied to a specific organisation. And lets not forget that these statistics are often produced by organisations who have a vested interest in data quality solutions.

 Just what is the cost to a business of holding a mis-spelt surname, an old telephone number, the wrong date of birth. Those of us who have worked in the data arena for many years share an underlying drive to simply get things right. We find it satisfying to see the results of a data cleanse operation – Garbage In – Gold Out. But asked to put a cost / benefit analysis together and we struggle to produce a sound financial case that will stand scrutiny. The fact of the matter is that the impact of poor quality data and the benefits of putting it right become entangled within the many other factors that impact business performance. And lets not forget, the data that we often focus on is data related to real people. And people are unpredictable.

Lets say we have a customer record for someone called “Jhon Smiith” – clearly we have mis-spelt his name. If we write to “Jhon Smiith” he may not be too impressed. Will he complain? He may do and we may have to correct his database entry – a bit of admin time – no big deal. Will he buy another product from us? Maybe yes – maybe no. Maybe he would never have bought anything from us again anyway. Maybe our deal is so good that he would never close his account no matter how we addressed him. Maybe he’s just too busy to do anything about it. The problem is we just cannot predict how individuals will react to us holding poor data on them. We like to argue that mis-spelt names and addresses lose us business and have an adverse impact on our brand. This may be so. But many people just accept this as being a consequence of dealing with businesses. It’s just a symptom of the modern world – nothing to get hung up about.

So when it comes to making a business case we often find ourselves floundering in the emotional, gut feel, “because its the right thing to do” sea of argument. There is nothing wrong with this. A great many decisions are made that have no financial basis whatsoever. But in order to avoid being shot down by the financial arguments we must construct a sound counter-argument which may be based around factors such as – brand reputation, business agility, net promoter scores, customer satisfaction etc. These may be “touchy-feely” factors but they don’t have to sound that way. The key point is that if you know the financial case is shaky, build a solid foundation from the emotional factors.

It is worth drawing an analogy between data quality and hygiene. It goes like this.

If we are ill, then being clean and hygienic will not on its own make us better. We need medicine to do that. But being hygienic to start with may have prevented us from getting ill in the first place. And being hygienic will speed up the recovery process by preventing re-infection. Being hygienic keeps us fit and in tip-top condition.

Don’t get me wrong. A financial business case is a strong business case and we should not shy away from including all the financial arguments we can lay our hands on. The usual suspects, though long in the tooth are still very valid and powerful. But spend time in honing those gut feel instincts to present a cogent and well argued case. You know it’s the right thing to do – you believe in data quality so let your passion shine through.

If you have any tips for building a data quality business case why not share them?

 

 

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8 Responses to Data Quality Business Case – A Hygiene Analogy

  1. Diane says:

    That is so right.

  2. Hi Andrew,

    This article is written very well. We had run a direct marketing campaign comparing how poor data quality causes long term health problems to diabetes build up. In some cases – experience Managers who have seen several analytics projects dont need any convincing. They are clear that project success requires good data. Even here – sometimes – the creation of single window view seems to be more important than cleansing the data. Financial computations can be done and should be done after hearing clients pain points. This then will allow the business rationalisation to be more tuned to users pain points and have more ready acceptance.

  3. Andrew Dean says:

    Thanks Uma.
    We hear all the time about CRM and Single Customer View projects failing. One of the usual reasons for failure is poor data quality. The CRM vision dissapoints when it doesn’t deliver the quality view that people expect.

  4. Krista says:

    Good article here. I like the facts you mentioned about how you will never know how a user will respond to even something as simple as a typo of their name. Personally, I tend to get a lot of direct mailings where mistakes are made in my last name and it causes me to question where the company acquired my mailing address because I never spell my name like that.

  5. Hello Andrew,
    This article reflects the reality, but people don’t accept the data anomalies / miss interpretation of data rules which organize itself the relationships like universe. We are at tip of the ice berg and do the judgment on data on the fly. This is a poor vision on data quality. Our job is to make the data quality at 20/20 vision, but facing unlimited challenges, including timeline on recognition of quality data.

  6. Andrew Dean says:

    Harshendu,
    We all strive to achieve 100% data accuracy. One of the difficulties in understanding how customers respond to poor data quality is that you only tend to get feedback from those who do actually complain. You do not hear from those who “can’t be bothered”. That is the point I am making regarding the difficulties in creating a sound business case. If you knew that every single data error cost you £n then the case would be easy to produce. But you don’t know that.

  7. Alan says:

    I work for a very succefull data company in UK and we are one of the best for our commitment to data quality. Below are the checks our data goes through which is very costly but keeps quality up to an exceptional level.

    TPS (every 14 days)
    •MPS (every 3 months)
    •Screening against standard industry mortality and suppression files
    •Suppression requests from consumers removed immediately
    •Goneaways returned from clients within 20 working days
    •Every April all databases are run against the edited Electoral Register to validate quality
    •Profanity checks and PAF validation on load of new data
    •Email address validation on capture
    •Telephone and Mobile number verification on capture and regularly thereafter
    •Telephone numbers are pinged on a rolling six monthly basis
    •“Clean Leads” our sophisticated lead management system to ensure the highest quality leads are delivered across multiple channels and sources
     
    Due to tighter regulations within the financial industry, especially with larger call centre operations, we have been asked to sign more “Due Diligence forms” on their behalf of potential clients confirming our credibility, and stability as a business.
     
    The MOJ (Ministry of Justice) number which we have been in receipt of, for 6 years is golden ticket into these companies, and  gets though compliance, it separates us from our competitors, of the 1200 DMA members less than 30 have to date.
     
    We are one of a very few companies that have a returns policy. Attached is our instructions for your piece of mind.

    Email me for more info.

    Alan.harrington@dlg.co.uk

  8. Pingback: Are You Using a Quicksand Foundation? | The Data Roundtable

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