Building a Robust Data Strategy Roadmap – Part III
December 5, 2014
In continuation to my last article on Building a Robust Data Strategy, let me meaningfully conclude it by highlighting some of the core issues which need to be addressed before data monetization could really be called our as a success and ROI is achieved.
Company needs to have the implicit and/or explicit statutory or legal right, or the ethical right, to divulge private consumer data – either personalized or depersonalized, individualized or at an aggregated level. Especially in industries where regulatory bodies have a heavy clout over what data is being used to cull out actionable insights or even the data flow within or beyond the walls of the organizations. Numerous articles, reports & surveys have highlighted how crucial is for businesses to operate within the ethical boundaries of data gathering or dissemination. Leave no stone unturned to see what policies/restrictions/guidelines are in place for the industry you operate in, how easy/difficult is to access data, and what are customer or end user reactions. You definitely do not intend to burn bridges with your existing customer base or repel away new prospects. Legal actions can be fatal to business at times. Be doubly sure what you are up for!
Do have a thorough understanding of the technological or hardware-related considerations to implement the strategy chosen to monetize the data. At times, organizations don’t have the requisite resources to execute on their strategy, may be because that’s not their core area of operation or it’s happening in silo’es across the organization which the business unit in question is not privy to. A complete landscaping exercise to understand the current state of business, what’s new in the market & what the competition is up to, what’s the future state & a step-by-step roadmap to mature technological prowess. In many cases, businesses hire external consultants or seek handholding by analytics service providers who have the requisite experience in recommending about the gaps & even executing on filling those. A thorough detailed analysis (but not analysis-paralysis) is crucial to the overall success.
At times, organizations sitting on huge pile of valuable data choose to make it available in the market (as another viable revenue model to monetize data). How much data to sell and how to determine costs vs. benefits in putting valuable data on the open market should be thought through. Be privy to the pros & cons of each approach & choose your business model accordingly.
Depending on its core competency, organization needs to identify at which level it wants to monetize the data in the data value chain. Data at each & every touchpoint in the value chain may have its own peculiar problems (missing data, incorrect data etc) and not all of it may be relevant. If your differentiator is “speedy delivery” of goods to your customers, focus on picking the right data sets across the value chain which helps streamlining operations, optimize inventory & transit time. Know what you are best at or what you are known for in the market and harness data capabilities to strengthen your business on that front.
Data Accuracy and/or Liability
Potential problems with inaccurate or directly or indirectly regulated data insights or products hitting the market place. Make sure that data assimilation, aggregation & cleansing exercise is robust enough to ensure the analysis/insights being generated out of it have a high probability of giving the right sense of direction to the business. At times, over-ambitious expectations or poor data quality can directly impact the quality of the outcomes. Garbage-in Garbage-out is the mantra & business managers should perfectly understand the gaps in the data & be cautious before making any solid commitments.
Perceived Market Value
For larger market opportunities, it is likely that an organization would want to play at the higher level in the data value chain. Umpteen times that completely derails the whole Analytics ROI & data monetization exercise. Focus should be specifically on business model(s) used to monetize the data than otherwise.
All the aforementioned considerations should set a good pretext to the data monetization exercise and may be the key to unlocking true value from data strategy initiative. In my subsequent edition, I shall bring to light the “Analytics Centre of Excellence” concept & how can organizations setup a full-fledged Analytics unit to deliver insights to departmants/LOB’s/functions across the business and also serve as a backbone to building a data-driven organization of the future. Stay tuned !