A day in the life of the gentleman banker was once described by the 3-6-3 rule – accept deposits at three percent, loan money at six percent and tee off at the golf course at 3 p.m. The financial services industry can rightfully state that it has come a long way since then. It has implemented technological innovation and managed risk in a constantly changing economic environment over several decades. The gentleman banker has since evolved into a sophisticated financial risk manager who works within a complex framework of rules and regulations with tens of trillions of dollars of assets under management.
Fintechs have moved at a much faster pace than banks in some areas. They have disrupted the financial services industry with user-centric solutions enabled by technology. These solutions emulate products and services offered by financial institutions. However, these remarkable examples of innovation have largely ignored the elephant in the room – regulation. Read more
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
Are you data-flooded, data-driven, data informed? Are you outcome oriented, insight driven or hindsight driven?
Are you a firm where executives claim – “Data is our competitive advantage.” Or sprout analogies like, “data is the new oil”.
The challenge I found in most companies is not dearth of vision… everyone has a strategy and a 100,000 ft general view of the importance or value of data. Every executive can parrot the importance of data and being data-driven.
The challenge is the next step….so, how are you going to create new data products? How are you going to execute a data driven strategy? How are you going to monetize data assets? What are the right business use cases to focus on? How to map the use case to underlying models and data requirements? What platform is a good long-term bet? The devil is in these details.
Everyone is searching for new ways to turn data into $$$ (monetize data assets). Everyone is looking for new levers to extract value from data. But data ingesting and modeling is simply a means to an end. The end is not just more reports, dashboards, heatmaps, knowledge, or wisdom. The target is fact based decisions, guided machine learning and actions. Another target is arming users to do data discovery and insight generation without involving IT teams…so called User-Driven Business Intelligence.
In other words, what is the use case that shapes the context for “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.” What are the right use cases for the emerging hybrid data ecosystem (with structured and unstructured data)?
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.
The following eight secular disruptive themes are what Goldman Sachs believe have the potential to reshape their categories and command greater investor attention in the coming years.
The Eight Themes:
- E-cigarettes – The potential to transform the tobacco industry
- Cancer Immunotherapy – The future of cancer treatment?
- LED Lighting – A large, early-stage and multi-decade opportunity
- Alternative Capital – Rise of a new asset class means growing risk for reinsurers
- Natural Gas Engines – Attractive economics drive strong, long-term penetration
- Software Defined Networking (SDN) – Re-inventing networking for the cloud era
- 3D Printing – Disruption materializing
- Big Data – Solutions trying to keep up with explosive data growth and complexity (Industrial Big Data and Personalized Big Data)
These eight themes – through product or business innovation – Goldman claims are poised to transform addressable markets or open up entirely new ones, offering growth insulated from the broader macro environment and creating value for their stakeholders.
Goldman focuses on the impact of creative destruction – a term made famous by the Austrian economist Joseph Schumpeter, which emphasized the fact that innovation constantly drives breeding of new leaders and replacement of the old.
The “real meat and potatoes” use cases behind big data actual adoption might be around B2B machine data management and Industrial analytics enabled by wireless, battery-free sensor platforms.
While social, consumer, retail and mobile big data get a lot of PR, the big data business cases around industrial machine data analytics or “things that spin” actually make economic sense. These projects tend to show tangible Return on Investment (ROI).
The concept of Internet-connected machines that collect telemetry data and communicate, often called the “Internet of Things or M2M” has been marketed for several years:
– I.B.M. has its “Smarter Planet” initiative
– Cisco has its “Internet of Everything” initiative
– GE has its “Industrial Internet” initiative.
– Salesforce.com has its “Internet of Customers” theme
To compete with GE….Hitachi, United Technologies, Siemens, Bosch, Schneider Electric, Phillips and other industrial giants are all getting on the band-wagon as the vision of M2M is now viable with advances in microelectronics, wireless communications, and microfabricated (MEMS) sensing enabling platforms of rapidly diminishing size.
The Bosch Group has embarked on a series of initiatives across business units that make use of data and analytics to provide so-called intelligent customer offerings. These include intelligent fleet management, intelligent vehicle-charging infrastructures, intelligent energy management, intelligent security video analysis, and many more. To identify and develop these innovative services, Bosch created a Software Innovations group that focuses heavily on big data, analytics, and the “Internet of Things.”
Similarly, the Schneider Electric focuses primarily on energy management, including energy optimization, smart-grid management, and building automation. Its Advanced Distribution Management System, for example, handles energy distribution in utility companies. ADMS monitors and controls network devices, manages service outages, and dispatches crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network.
Industrial Internet – making smart use of sensors, networked machines and data analytics – is the big vision, but the business driver is in no unplanned downtime for customers.
- 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.