Data Innovation Life Stages

Digital disruption has changed the world like never before. People are connected with things, Things are connected with Smart Products and Digitized Services. Enterprises are realizing how important it is to compete on real data innovation and the value generated from it. Many of them are realizing they have a long way to go.

Here’s a quick way to see where your enterprise is on the Data Innovation Maturity Curve. Obviously, higher the level, more the Return on Data Investments.

  • At this stage, only few identified or designated users have enjoyed some limited success. Typically, they are analysts who want to understand the big picture and have little contact with the day-to-day operations of the business. They rely mostly on operational reports with monthly views; so whatever insights they gain are in few weeks in arrears.
    • Limited power users by functions and levels with some database structure based on extracts from transactional systems
    • Dependent on IT, No Data Maturity roadmap with an emphasis on monthly reports & MIS
    • Management by anecdote with spreadsheet anarchy
    • Legacy reporting and MIS environments involving manual and long consolidation cycles
    • What enterprises are doing in this stage is - tool evaluation, business case, emerging technology

    Outcome : Understanding of ‘what happened’

  • At this stage, the real benefits of data coming from multiple transactional applications start to emerge. The CXOs begin to realize that having certain metrics could help them improve outcomes. The data they work with, however, limits them to mostly lagging indicators, and possibly a few leading indicators. Instead of outdated reports, managers receive actionable insights that help them make more effective operational and tactical decisions.
    • Set of technologies & tools that are under leveraged and not been able to reap the benefits of investments
    • Report findings up to C-suite and enterprise-wide usage begins
    • Mostly lagging indicators, a few leading indicators, some advanced KPIs
    • Enterprise data/information Strategy, Roadmap & migration planning
    • Enhance & mature analytics practice through optimum utilization of current data and reporting environments.

    Outcome : Understanding of ‘what happened and why.’

  • When analytics reach this stage in an enterprise, they are no longer a curiosity or a pilot program. More emphasis is given on correlating data coming from transactional, social and external sources. This stage aims at deriving insights for future business with predictive analytics and application of algorithms and data science techniques. Top management gets a deeper view of important trends from the market based on internal as well as external data feeds. At this level, the enterprise is competing on data innovation stage.
    • Continuous self-updating predictions driven by learning algorithms
    • Empowering business by developing in-house rapid deployment products & accelerators with DIY / ease of configurations without major IT involvement
    • KPIs broadened to include customer, economic and strategic focus areas and ROI of data management and data science becomes clear
    • Mature analytics practices with set of executive dashboards, business insights and scenarios for better decision making based on operational and social data

    Outcome : Understanding of ‘What is happening tactically and strategically & what may happen based on the correlation and past experience’

  • When an enterprise undertakes Digital Transformation initiatives or new age companies/innovators are disrupting with innovative business models, they reach this stage of maturity, it doesn’t stop there. Enterprise wants Industrial, Textual & Relational data to be accessed and leveraged on for real-time monitoring & predictions. As ROI compounds, the number of users, use cases, and new data originating from news apps and industrial devices continues to accelerate to get a complete market view, customer view, product view.
    • Quantitative and qualitative metrics with simple, accessible user experience
    • Partner for co-innovation, co-creation of IP and solutions for futuristic market & customer scenarios with emerging technologies
    • Disruption of business models with use cases of IoT, Industry 4.0
    • Market leader with need for constantly being ahead for competitive advantage

    Outcome : Actions for maximizing business outcomes through new and transformed business models identified from transactional, industrial, social data together

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