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
New Technologies | New Possibilities
As a C-level executive, it’s becoming clear to me that NoSQL databases and Machine Learning toolsets like Spark are going to play an increasingly big role in data-driven business models, low-latency architecture & rapid application development (projects that can be done in 8-12 weeks not years).
The best practice firms are making this technology shift as decreasing storage costs have led to an explosion of big data. Commodity cluster software, like Hadoop, has made it 10-20x cheaper to store large datasets.
After spending two days at the leading NoSQL provider MongoDB World event in NYC, I was pleasantly surprised to see the amount of innovation and size of user community around document centric databases like MongoDB.
Data Driven Insight Economy
It doesn’t take genius to realize that data driven business models, high volume data feeds, mobile first customer engagement, and cloud are creating new distributed database requirements. Today’s modern online and mobile applications need continuous availability, cost effective scalability and high-speed analytics to deliver an engaging customer experience.
We know instinctively that there is value in all the data being captured in the world around out…no question is no longer “if there is value” but “how to extract that value and apply it to the business to make a difference”.
Legacy relational databases fail to meet the requirements of digital and online applications for the following reasons:
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
In this summer’s blockbuster movie “Edge of Tomorrow,” a PR executive played by Tom Cruise goes through innumerable time loops to become a soldier by being reborn every time he is killed. In the context of software startups, successful products are built through repeated testing and improvement. Those that can do the most iterations without dying become the needle-movers.
The evolving Internet of Things (IoT) ecosystem presents opportunities for startups that can create sustainable solutions. Further to our article, “Internet of Things Needle-Mover Opportunities,” we looked at companies that will form the basic foundation of technologies that address the following five IoT challenges:
- Privacy and security
- The power barrier
- Data analytics and management
- Interoperability and integration
The automated vote-counting machine was designed by Thomas Edison in 1869 to replace roll call voting in the U.S. Congress and was never used. The motor scooter was designed in post-war Italy to be a motorcycle for women and became a revolutionary transport mechanism for a larger population. The Java programing language was originally designed in the 1990s for use by set-top boxes. And eBay was created to sell Pez dispensers. History has many examples of how original use case definitions became irrelevant in the face of market economics. Like any other new technology, the Internet of Things (IoT) will create an ecosystem with its share of winners, losers, survivors — and needle movers.
Today, use cases abound on how the IoT’s connected devices can create economic value. Some analysts talk about white spaces of solutions that span industrial, commercial and consumer applications. Others talk about fundamental challenges in delivering on the promise of IoT. While white space use cases will have hits and misses, IOT-enabling technologies represent a much larger opportunity for innovative value creation.
Another day, another data breach. Just received another “We’re sorry you got hacked”…letter.
This is the fifth letter I have received in the past 3 months: Forbes.com, Target, Neiman Marcus, credit card company and a previous employer. What is going on?
Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify? Why is the attack signature – sophisticated, self-concealing malware – so difficult to spot? Do firms need to invest in NSA PRISM type threat monitoring capabilities?
The three impediments to discovering and following up on attacks are:
- Volume, velocity and variety – Not collecting appropriate security data
- Immaturity and not identifying relevent event context (event correlation)
- lack of system awareness and vulnerability awareness
Obviously… where there is pain…there is opportunity for entrepreneurs see below – data from IBM). There is a growing focus on big data use case for security analytics after all the breaches we are seeing. General Electric announced it had completed a deal to buy Wurldtech, a Vancouver-based cyber-security firm that protects big industrial sites like refineries and power plants from cyber attacks.
Here are three recent examples that I was personally affected by – Forbes, Target, Neiman Marcus.
“Google, Facebook are really big data companies, not software companies. They collect data, process it and sell it back with value added extensions. They don’t have better algorithms. They simply have more data.” — Anonymous
The convergence of cloud, social, mobile and connected computing has sparked a data revolution. More than 90 percent of the world’s data has been generated over the last two years . And with a projected 50 billion connected “things” by 2020 , the volume of data available is expected to grow exponentially. This proliferation of data has created a vast ocean of potential insights for companies, allowing them to know their customers in a whole new way.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data (Cloud, On-Premise, Hybrid IT) is challenging. It’s a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight.
Help people uncover, see, understand and visualize data presents a broad and momentous market opportunity….call this user-driven discovery. Take for instance, Facebook (like Amazon.com) builds a custom Web page every time you visit. It pores over all the actions your friends have taken—their postings, photos, likes, the songs they listen to, the products they like—and determines in milliseconds which items you might wish to see, and in what order. Is this the future for every firm…..
The opportunity is simply getting bigger by the day. Every customer interaction is generating a growing trail of data (“data exhaust”). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response is a potential digital bread-crumb of opportunity.
Now let’s flip the context. A typical mobile user check their phone interface 150 times a day for updates. A Gen Y or Millenial user obviously much more than a Gen X user. The consumption patterns for information are changing continuously. Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an “attention deficit economy” where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$.
As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data, wearables data….is relentless. As a result there are a lot of new initiatives and companies. I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size. Product and services firms are also interested in opportunity sizing as they create new offerings in the data rich world.
I thought i would share a mashup of industry and market sizing data i have collected so far.
- How big is the overall market for Analytics, Big Data?
- How big is the market for Digital Customer Interaction or Engagement?
- How big is the market for Mobile and Social Intelligence?
- How big is the market for Wearables?
- What is growing fast, faster and fastest?
All good questions as services firms think about digital strategy, analytics and future state. You always want to be in the “hot” area… selling is easier, valuations are richer, revenue growth percentages exponential.
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.