Data management implementation comes with pitfalls you just won’t spot until it’s too late.
Google, Amazon, Apple, Facebook and Microsoft—some of the usual big-league suspects in any ‘most valuable companies’ lineup.
Or, as this 2017 article by The Economist—commenting on the ‘data as the new oil’ topic — "giants that deal in data."
The article goes on to claim that, in just the first quarter of 2017, these data-guzzling tech giants collectively racked up over $25 billion in net profit.
How did they do it? Let’s just say it wasn’t your free Gmail account. These companies know how to implement a data management process that unifies their goals with the goals of the customers.
Data management as a route to customer experience
The world’s most successful businesses can be described as such because they’re in touch with one resounding truth:
Customers don't care about your infrastructure or data problems. They just want you to know who they are, what they asked of you, and how to fix things that go wrong.
In other words: Customers expect to be provided with the best possible experience at every interaction—and that means implementing an effective data management process flawlessly.
Why is data management back in fashion?
The challenge for any growing organisation, big-tech or not, is maintaining holistic, well-organised real-time access to the right data. When that happens, data fuels financial decisions.
So, what’s the hold up? Why isn’t every organisation on the planet chugging steadily along the path of their ‘data-to-decisions’ journey?
Application centric vs data centric
It’s not that organisations don’t have the data they need—they do. So much of it that they have endless copies of the same data scattered across applications—that’s the end result of a traditionally application-centric approach to data management.
Application-centricity isn’t so much ‘someone’s fault’. It’s merely a consequence of how application vendors build things, with each application having its own database application-specific data schema.
So… what’s the answer?
Big data? That was fashionable for a while, particularly amongst the big players in tech. Though it fails to solve the problem of maintaining holistic, well-organised real-time to the right data.
Big data is useful for telling you certain insights tomorrow. Not today’s insights now. For that you need to know how to implement a data management process efficiently.
And that’s why data management implementation is all the rage again.
How to implement data management process like the big tech giants
There’s no secret to data management process implementation. It isn’t rocket surgery and you needn’t be a Forbes 100 company to achieve it.
You just need to consciously have the same realisations about customer and data-centricity as the big players, before enacting systematic data management change.
1. Define your data architecture
When the world’s biggest companies define a data architecture, they’re creating a blueprint for how to treat data at rest, data in motion, data sets and how all of that relates to data-dependent processes and applications.
2. Assign roles and responsabilities
To have any chance of consistency, certainty and strong data-quality when implementing a data management process, you must be clear about who’s responsible at every stage. That means assigning clearly defined roles and responsibilities that add value to data without contradiction and duplication.
3. Define data naming conventions
Little details count. Tiny naming errors of files and data can amount to huge expenses and corrective work further down the road. When implementing a data management process, standardise file naming and decide how changes will affect a file’s name.
4. Collect data
Now it’s time to start collecting data. First, though, you’ll need to define what can and should be collected, why, and what the expected output will be. This is by far the most important step on which your data-centricity approach and ‘data-to-decisions’ journey hinge.
5. Prepare data
Once you’ve collected your data, you need to knock into shape to increase its utility. For example, you’ll need to validate raw data by checking it against another source. If that’s not possible, you can try exploratory data analysis to check accuracy. The outcome needs to be data you can use to process further.
6. Process data
Data processing comes next in your data management implementation strategy. Now it’s time to convert your data set into something a specific software can understand. If you’re stuck at this point, we can help.
7. Analyze data
Now the magic happens. This is where you try to interpret meaningful results, often through a specific interface like our very own Finworx or Retailworx that sit on top of a Microsoft Power BI. Ask us for a demo and we’ll show you how Enterprise’s BI platforms help you find data patterns that unlock smarter financial decision making.
8. Interpret data
Data interpretation requires a trained eye, though the right business intelligence tool can make data interpretation incredibly intuitive. For example, Enterpriseworx’s BI analytics tool, RetailWorx, helps you easily interpret key insight, and trends so you can spot threats and opportunities easily in real-time financial events in the field.
The number-9 Bonus Step: Data-journey Partnership
These 8 steps are just the theory. Data management implementation comes with pitfalls you just won’t spot until it’s too late. If you’re realising the need for some effective data management process housekeeping, speak to us about becoming your ‘data-to-decisions’ partner.
Even if you already have a business intelligence tool in place that’s not from us, it’s worth querying our expert guidance to help you optimise your BI outputs.
Wherever you are in your data journey, we’re here to help you get to the next level.
