India's largest platform for AI & Analytics leaders, professionals & aspirants

Sign in

India's largest platform for AI & Analytics leaders, professionals & aspirants

3AI Digital Library

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.  1

 

Privacy Concerns

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!

Technology Constraints

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.

Intellectual Property

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.

Core Competency

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 !

Related Posts

AIQRATIONS

    3AI Trending Articles

  • Cybersecurity Challenges: Fourth Greatest Danger to Global Economy

    The 2021 World Economic Forum (WEF) Global Risk Report, used for over a decade by organizations around the world as a risk assessment tool, has named “cybersecurity challenges” as the fourth most pressing danger to the global economy. The ranking is determined by responses to a survey conducted among various stakeholders and organizations affiliated with […]

  • Chess and AI

    “It’s just a machine. It has no consciousness or feelings as we understand them. We have specific connections in our brain that make us react according to the circumstances, the situations we are experiencing. We interpret them as pleasure, pain and all other kinds of emotions. We would have to invent a new word to […]

  • Daimler India opens new GCC

    Daimler India Commercial Vehicles (DICV) has opened a new Global Capability Centre in Chennai with 165 seats to meet growing demand for Indian-based shared services. Spread over 20,000 sq.ft., the facility is designed to house the company’s ‘shared services’ business stream. Shared services refers to the various services DICV exports to other Daimler entities around […]

  • Top four focus areas to help you shape a data-driven enterprise

    Author: Praveen Reddy, Vice President (Digital – data, analytics, and cloud) | Genpact As we navigate the world of data, some significant trends are manifesting with respect to data ownership and utilization. There are multiple triggers for these trends – ever-rising complexity of data, lack of proper intelligence concerning data assets, need for accelerated digital transformation, […]