Data Strategy: A strategic assetSudhanwa Rajurkar
Mckinsey just came out with a study that found that the companies they survey could attribute 20% of their bottom line to AI implementation
The buzz created around AI, ML is finally helping companies of all sizes to increase their revenue, OR reduce their costs.
CoreView had an opportunity to develop ML-based business solutions for a variety of small and mid-sized customers across industries and functions like Cyber Security, Digital Marketing, Hospitality, Services …to name a few.
Each of these had its readiness at different levels. Most had the business strategy aligned, executive blessings, and decisions to invest in AI, ML-based business solutions. But their data readiness was very different.
Few had had already an effective data strategy in place, with loads of labeled, quality data, accessible.
Some of them had the raw data, but no clue what to do about the data.
There were others who had no data, no strategy in place.
You don’t have to be a genius to guess, where we could implement the AI, ML solutions more effectively and efficiently.
But an important point to highlight here is, companies with business outcomes aligned data strategies in place stand to extract the most ROI from AI, ML investments.
But how do small and young businesses decide their data strategies? Easier said than done.
Data strategy is very fuzzy unless it is tied to business outcomes that you need to achieve.
Defining a Data Strategy is a top-down approach. You as a business leader need to clearly identify the business outcomes you are looking for e.g. Reduce burgeoning Marketing costs without reducing effectiveness, optimize pricing based on demand, macro-economics, demographics,
This helps in identifying the data that is needed to be captured, cleaned, labeled, stored, and made accessible for business functions.
Strategizing your data for building business value is a journey, It cannot be defined and completed in a fixed time.
Not all Small businesses create these Data Strategies upfront, pro-actively. It needs a long-term vision from leadership.
Sometimes there are triggers that give rise to data strategies; Triggers could be sustained losses, increased overhead costs, stagnating revenues …etc, which force the leadership to start looking at innovative AI, ML-driven strategies. It is a very natural way of formulating your Data Strategy, the advantage is that it is tied to solving a real business challenge.
These triggers also lead to extending your existing Data Strategies.
For companies who do not have any existing data strategy or raw data, but want to use AI, ML-based business optimizations, innovations; it is important that Data Consultants like CoreView educate them about the importance and necessity of a business aligned Data Strategy, before embarking on the creation of the AI, ML-based solution/product. Companies who don’t pay heed to this expectation and dont put a Data Strategy in place and don’t allow it the time to grow and mature; are putting their hard-earned dollars into a project which is bound to fail.
Data Strategy itself is a strategic business asset for an organization. The right Data strategy in place will enable building an AI, ML product solution.
The quality data itself can be an IP for an organization, and the availability of this data can fuel building futuristic data products. Data-driven companies like Google and Facebook are classic examples of what a great Data Strategy can do for an organization.
IP and or Business Innovation is not achieved without a sound investment of time, resources, and efforts.
Data Strategy is a strategic investment needing significant time, resources and efforts, for any AI, ML-based business investments.
Companies who have attributed their significant bottom-line improvement due to AI, ML-based innovations, are the ones who have made this investment, knowingly and purposefully.
CoreView can help you build your data strategy if you don’t have one in place.