The challenge today for leaders in every enterprises is (a) how to monetize data? (b) how to create enterprise class platforms instead of sandboxes? Basically, how can data, analytics and insight drive digital operations and digital transformation?
The paradox is interesting. While leadership is struggling with value creation, the long term data trends are favorable: (1) data continues to grow exponentially and outstrip our ability to convert into insight; (2) data consumption is evolving… Consumers and employees have more interactions with data through mobile apps than they do through desktop browsers; (3) Analytics – predictive and prescriptive – is gaining traction in several industries and business processes.
In the first wave of excitement around big data, there is a massive amounts of investment in stand-alone pilots and sandboxes. Some experiments worked, but many failed to deliver. The linkage between analytical projects and the everyday business applications (systems of record, systems of engagement, systems of insight) have mostly been missing.
In the next wave, we are seeing a tighter alignment or foundational underpinning between analytics (even machine learning) and traditional business applications. Take for instance Salesforce. Machine learning has become increasingly important to Salesforce, which has acquired PredictionIO, RelateIQ and Tempo AI, among other companies.
This implies a massive transformation wave (and upgrade cycle) across existing:
- Systems of insight (Reporting, BI, Analytics platforms)
- Systems of engagement (CRM, SFA)
- Systems of record (ERP)
The bigger transformation challenge is around how to systematically clear the bottlenecks in each of the above so that end-users can (1) access real-time data; (2) slice and dice the data for actionable insights from any device, anywhere; (3) convert the insights into guided decisions.
The directional strategy is clear, but can leadership get behind it and implement it swiftly and effectively?
This impending transformation is both exciting and daunting at the same time. Application development and delivery (AD&D) teams are overwhelmed. Leadership (in most corporations) in terms of vision or directional clarity is often weak or missing. Strained relationships and misaligned business and technology teams is unfortunately the norm, not the exception.
Something is out of whack.
What I have observed in multiple engagements and research is that every year, billions of dollars are being spent with the consulting industry on establishing a corporate data strategy. Millions of hours of leadership time is invested in the strategy effort.
Even more billions are being spent on the core foundational “data lake” strategies – using Hadoop to create a large scale data dump. The more advanced form of this is to add Master Data Management (MDM) on top to create a “single golden view” of customers, employees, products or accounts. The hope (and prayer) is that this will enable companies to derive a more accurate picture of their business. This is the hypothesis behind many “data lake” initiatives. The results and business value have been sketchy so far. So, is the strategy wrong or execution flawed?
Executing the data/analytics strategy around systems of engagement, record and insight is where the pain and the largest costs to the organization come into play. This is where discipline and talent becomes critical, and where competitive advantages are either won or lost. But most firms starve the application development and delivery (AD&D) teams — limited budgets, basic talent to do cutting edge solutions, unrealistic deadlines, constantly changing requirements. Few invest in product or program management to ensure the entire organization is involved, aligned, and ready to make it happen?
As a result, most corporations today have an awfully poor batting average when it comes to analytics projects or data informed business processes. This also may lead to the frequency of failure of most large-scale data-driven corporate change initiatives.
The hype cycle is not a new phenomenon, but one that repeats itself with each innovation that somehow captures people’s imagination. We have to be patient in the next 2-3 years and dig ourselves out of the big data Trough of Disillusionment 🙂
I am bullish on data long term. The future is becoming more data-driven everything. Every industry (financial services, healthcare, retail, industrial) is going digital powered by data.
Source: It’s Time To Upgrade Business Intelligence To Systems Of Insight Supercharge BI With Agility, Big Data, And Insights To Drive Action by Boris Evelson, July 20, 2015
Data-driven business processes are not a nice-to-have but a need-to-have capability today. So, if you’re an executive, manager, or team leader, one of your toughest assignments is managing and organizing your analytics and reporting initiative.
The days of business as usual are over. Data generation costs are falling everyday. The cost of collection and storage is also falling. The speed of insight-to-action business requirement is increasing. Systems of Record, Systems of Engagement, Systems of Insight are being transformed with consumerization and digital.
With this tsunami of data and new applications, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action. This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses.
Better/Faster/Cheaper Analytics Execution
Decision support needs better visualization. Scorecards, Dashboards, Heatmaps, Alerts, Management Reporting, Operations and Transactions Reporting are all enterprise example of data visualization outputs.
Some data visualization examples include:
- Data Scientist — uses “R”, a programming language used for statistical modeling, to understand traffic flows and congestion patterns and advise on options to improve travel times for Amazon.com Local delivery drivers.
- Pharmaceutical Sales Representative — uses QlikView on an iPad to access current industry sales trends and doctor prescription history while on a sales call with a busy physician.
- Healthcare Chief Medical Officer — uses Tableau Software to analyze all aspects of hospital performance including population management, emergency room effectiveness and Affordable Care Act compliance.
- Crime Analyst— uses Microstrategy to maintain a consolidated view of crime levels and optimize staffing allocations to dispatch police into high crime areas.
- Retail Store Manager — uses QlikView to analyze which products are selling best which impacts store assortments and which products get featured vs which ones get discontinued.
- Telecom Customer Service Agent — uses Spotfire to monitor call center statistics and how it translates into customer satisfaction and retention.
Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.) will reinvent healthcare as we know it. I expect the healthcare transformation to start incrementally and develop slowly in sophistication. Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.
Why change? Consider this:
- Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.
- Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees. Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.
A satisfying experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest (and fan) personalization strategy leveraging wearables (and analytics) to track, measure and improve the overall park experience. The goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.
Wearables are the next big thing. The new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.
Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate biometric data.
What most people don’t understand is how powerful wearables (coupled with analytics) can be in designing new user experiences. Businesses thrive when they engage customers by creating a longitudinal predictive view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.
Wearable Computing at Disney: MyMagic+
Self-tracking, Seamless Engagement and Personal Efficiency improvement’s new frontier is Personalized Big Data and Digital Health. This is really becoming a viable idea around wearable and sensor computing and the basis for new data platform wars.
The new platforms for digital life or data driven life — that collect, aggregate and disseminate — will cover a wide range of new User Experience (UX) use cases and end-points… medical devices, sensor-enable wristwear, headset/glasses, tech-sensitive clothing. All of them are going to collect a lot of data, low latency analytics, and enable data visualization. Several new firms are entering the activity tracker market LG (Life Band Touch), Sony (the Core), Garmin (Vivofit), Glassup, Pebble, JayBird Reign etc.
Data collection is just one piece of the solution. The foundation for personalized big data is Descriptive and Predictive Analytics. Ok…What do i next? what is the suggestion? in the form of predictive search (automated deduction or augmented reality).
How do i discover useful patterns, analyze, visualize, share, query and mobilize the collected data? A wide range of start-ups – Cue, reQall, Donna, Tempo AI, MindMeld, Evernote, Osito, and Dark Sky – and big companies like Apple, Google, Microsoft, LG and Samsung are working on predictive apps — aimed at enabling new robo-assistants that act as personal valets, anticipating what you need before you ask for it.
- How do I monetize my data? How do we turn data into dollars?
- What small data or big data monetization strategies should I adopt?
- Which analytical investments and strategies really increase revenue?
- What pilots should I run to test data monetization ideas out?
Data Monetization is the process of converting data (raw data or aggregate data) into something useful and valuable – help make decisions (such as predictive maintenance) based on multiple sources of insight. Data monetization creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell lift; or prevention of equipment breakdowns).
But, data monetization requires a new IT clock-speed that most firms are struggling with. Aberdeen Research found that the average time it takes for IT to complete BI support requests, with traditional BI software, is 8 days to add a column to a report and 30 days to build a new dashboard. For an individual information worker trying to find an answer, make a decision, or solve a problem, this is simply untenable. For an organization that is trying to differentiate itself on information innovation or data-driven decision making, it is a major barrier to strategy execution.
To speed up insight generation and decision making (all elements of data monetization) business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own stream of questions and follow their own path to insight. Unlike traditional BI that provides dashboards, heatmaps and canned reports, these tools provide a discovery platform rather than a pre-determined path.
Also companies like Marketo which create marketing automation software are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals find more future customers; to build, sustain, and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. And in many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across multiple fast-moving digital channels requires an engagement strategy enabled by data and analytic insights.