Big or small — the same rules apply: How to streamline your data management processes for success
Gut-instinct alone is not enough. Effective decision-making also requires hard factual intelligence. Which is why millions of businesses strive to become data-driven organizations.
According to 2017 research by TDWI, organizations are making real progress -- but there remain notable obstacles. A third of survey respondents still believe they’re not close to being data-driven. The biggest reported barriers? Leadership, technology and skills. And also bad data quality, which according to Gartner, causes 40% of business initiatives to fail to realize their targeted benefits.
Before you can maximize insights from Business Intelligence and Analytics, you need to ensure your teams have confidence in your customer data. And that demands ensuring consistent data capture management practices are in place.
The high cost of poor data management
I see the same challenges crop up again and again. Companies wishing to be data driven use inconsistent (data capture) processes that result in fragmented information and data silos. Others depend on a conglomeration of apps that are linked manually or via spaghetti coded interfaces, without any single point of reference to ensure data consistency.
Many companies also depend on names (or emails) as the primary customer identifier. However, customers are not static entities frozen in time; they’re dynamic. People get married. Businesses change address. By one estimate:
- 70% of people change at least one piece of business card information every year.
- 7-10% of consumers and businesses relocate every year.
- 2-4% of all customer data becomes stale on a monthly basis, for an annual rate of 25-50%.
For example, one medium-sized company I advised had five different CRMs. They were duplicating clients across each one, with no consistent customer identifier. When from an enterprise perspective you wanted to see what a person or company was doing, it was a huge challenge, requiring an unreasonable amount of work to cleanse and piece together disjointed records.
And that client is hardly alone. Nearly half of 2017 TDWI survey respondents said their users spend at least 61% of their time finding and preparing data. Imagine if you could reduce that preparation time by half and have users focus on actually using their data.
The need for mindful data governance
Reform doesn’t need to be complicated. Whether you’re a big company or small company -- the same rules apply. Just a little effort and thought can go a long way.
First, step back and take a look at the bigger picture.
From the moment data enters into your system to when it’s used for things like market segmentation, lead management and sales forecasts -- who provides your data? And who is going to consume it?
A huge mistake is to assume data management is just an IT problem. In fact, it encompasses everyone. Enlist the input and help of your business users. They’re the ones who will garner the benefits; who understand the true business context and the meaning of your data; and who can provide critical guidance on what’s needed and what’s desired.
Also, identify your key data sets. For most businesses, that’s client data and product data. By mastering just these two areas you can often drive efficiency and success across all aspects of your business.
In an ideal world, you would have all new client data captured inside your single CRM where you could verify its accuracy before flowing that data to other parts of your organization. This follows an efficient hub-and-spoke architecture. Everything passes through what’s referred to as a System of Record -- a centralized system of verified information serving as your authoritative truth for that data.
Even with this architecture in place, you will still need to implement rock-solid data capture processes and have them followed across each contact with the customer. Since even good data decays over time, it is critical to identify your key business information and make data cleansing and verification an integral part of your workplace culture.
If you don’t deal with duplicates, missing names and outdated contacts -- no matter how good your technology -- you are still going to get unreliable information that will prevent you from conducting confident forecasts and analysis.
Four steps to becoming a data-driven organization
In summary, companies wanting to become a data-driven organization will need to follow these steps:
- Define your business keys -- Identify what data sets are critical for your business and set clear rules for its capture and record keeping. Implement a unique Customer Identifier for each individual or client across your company. I recommend using a unique number as names and emails can and do change.
- Define your Source of Truth -- Once you’ve identified your key datasets, you will need to centralize information into your System of Record. That way, you will always have a place to verify information when data is incomplete or conflicts.
- Align your business processes and people -- Update your business processes and data flows to ensure the System of Record is kept up to date. And ensure your people are trained on the updated processes.
- Set clear standards for data capture and checking -- Consider assigning a data steward if your business does not already have one. Even small businesses stand to gain from having someone commit time every week to vetting lists of contacts and running your software’s deduping and entry verification tools.
Trevor Broatch is President of Missing Link Consulting and a certified Data Architect, Cloud Data Consultant.