Data, an Asset, or a Liability?
This is the second piece of a 5-part series on plugging the obvious but overlooked gaps in achieving digital success through a refined data strategy. You can read the first one here.
Data strategy is the anchor to your digital journey. As established in the first post of this series, there is very little latest technologies and skilled experts can do when your data strategy is falling through the cracks. So as you get set to build the foundation of your digital journey, the data strategy, here’s bringing your focus to the first of the four most common misses while putting together a data strategy.
We have all, at some point, heard this piece of advice: start by asking what your data can do for you. And sticking to this idea, you might have actually started or are planning to start your digital transformation journey relying on the type and amount of data you have at your disposal right now. Let me tell you why you need to apply brakes to this line of thought right now.
Data doesn’t come before your strategy, it comes after.
Data is your tool to achieve a strategic goal. This means that no matter the type, quality, and quantity of data you’ve got, you start with a business problem. Let the business problem help you build your data strategy and then, as the next step, get into collating data. But this does not mean you gather all the data you can possibly get your hands on. Filter data that is relevant to your strategic goal. And how do you do that?
By establishing KPIs and metrics.
What are KPIs & metrics? Are they the same?
No, they aren’t. Two overused terms like many others in the business world, Key Performance Indicators (KPIs) and metrics are crucial for the success of your data strategy. But you must understand here that both don’t mean one and the same, and shouldn’t be used interchangeably.
KPIs help you track your progress against the data strategy that you’ve put together. Let’s say, it is the key to the larger picture. Whereas your metrics are more tactical and specific to tracking a particular process.
One of the simplest ways to express the relationship between KPIs and metrics would be to say that metrics support KPIs which, in turn, support your overall data strategy. For instance, your business objective may be to acquire new customers through your online channels. In this case, the unique number of visitors on your online channels can be considered a metric whereas the number of new leads generated would be your KPI.
KPIs & metrics determine your data sources.
Once you have a handful of KPIs & metrics to determine the performance of your organization against your data strategy, it is now time to filter out the data that’ll support these KPIs & metrics. Go fishing in your sea of data to capture the few and fine data sources that will give you relevant insights into your chosen metrics. If you are already tracking the necessary data sources, it is well and good and in case any new data source needs to be tracked, you’ll need to review and explore possibilities to make the same happen.
Forget asking what your data can do for you.
Without a sound data strategy, your sea of data can do little or nothing. Let’s bear in mind: data is a lighthouse that guides your ship of digital transformation in the right direction. But it cannot determine the ship’s route, speed, or destination. That’s what your data strategy is for.
Let’s uncover if your data is truly an asset or a liability?
Without a doubt, data is your golden key to getting digitally fit. But without a sound data strategy, even the best and vast data cannot tone up your organization’s operational muscles a case established in the first post).
Sadly, the trance of data is so alluring that even the best of minds make the grave mistake of overestimating its potential. The truth is, your sea of data is only as good as your data strategy.
Without a refined data strategy, no amount of data can drive any transformative change within your organization. But a lot of times (more than we would want to accept), the quest to get the best out of data can be so blinding that organizations don’t realize when it has gone from being an asset to a massive, capital, and skill-sucking liability.
A reality-check of your investments.
The investments you’re making in data can be broadly categorized into two: ones focused on data acquisition and maintenance and those focused on translating the collected data into actionable insights. While it does not take a genius to know that the real value of data lies in the latter, surprisingly, larger investments are made in data preparation than in its analytics. The millions spent in acquiring new CRM software, machine sensors, cloud infrastructure, etc. are all directed towards the collection & management of data when in reality the analysis is where the ROI lies for businesses. To an extent that a study by Forrester reveals that almost 60-73% of all the data collected goes unused. Whether this disparity is a result of slow cultural change within organizations, lack of skills to read & understand data or anything else, it is a matter of serious concern for businesses. Bill Schmarzo, in one of his videos, has done a great job in depicting this stark reality through a relatable visual representation.
Shift your data gears to analytics.
To truly monetize your data investments, you must turn your focus intently towards advanced analytics. Most organizations are already collecting enormous volumes of data. Now it’s time to derive value from this data. Squeeze and wring the data to uncover valuable operational, customer, product, and service-related insights. Insights that will drive better decision making to enhance operational efficiency, discover new revenue opportunities, mitigate risks, or improve customer experience.
Data is, undoubtedly, the golden key to your digital fitness. However, this holds true only when it starts unlocking valuable insights that can be applied to enhance business performance. Until then and even if you are sitting atop a growing mountain of data, it is still a liability. The real shift to monetizing data can truly happen when we start seeing ourselves as more insight-driven organizations than data-driven ones, to be discussed at length in the next post.
By Anita Raj
Anita is a Germany-based technology evangelist with more than a decade of experience in Cloud, Big Data and AI. Previously, Anita has held product leadership roles in international markets such as UK and Silicon Valley for Fortune 100 companies such as Progress Software, Dell EMC and Infosys. LinkedIn Twitter @anita4tech