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
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.
The bleeding edge of data and insight innovation is around next generation digital consumer experience. Consumer behaviors are rapidly evolving….always connected, always sharing, always aware. Obviously new technology like Big Data drives and transforms consumer behavior and empowerment.
With the influx of money, attention and entrepreneurial energy, there is a massive amount of innovation taking place to solve data centric problems (such as the high cost of collecting, cleaning, curating, analyzing, maintaining, predicting) in new ways.
There are two distinct patterns in data-centric innovation:
- Disruptive innovation like predictive search which brings a very different value proposition to tasks like discover, engage, explore and buy and/or creates new markets!!
- Sustaining innovation like mobile dashboards, visualization or data supply chain management which improves self service and performance of existing products and services.
With either pattern the managerial challenge is moving from big picture strategy to day-to-day execution. Execution of big data or data-driven decision making requires a multi-year evolving roadmap around toolset, skillset, dataset, and mindset.
Airline loyalty programs are a great example of multi-year evolving competitive roadmaps. Let’s look at BA’s Know Me project.
British Airways “Know Me” Project
British Airways (BA) has focused on competitiveness via customer insight. It has petabytes of customer information from its Executive Club loyalty program and its website. BA decided to put customer big data to work in its Know Me program. The goal of the program is to understand customers better than any other airline, and leverage customer insight accumulated across billions of touch points to work.
BA’s Know Me program is using data and applying it to customer decision points in following ways:
- Personal recognition—This involves recognizing customers for being loyal to BA, and expressing appreciation with targeted benefits and recognition activities
- Personalization — based on irregular disruptions like being stuck on a freeway due to an accident – A pre-emptive text message… We are sorry that you are missing your flight departure to Chicago. Would you like a seat on the next one at 5:15PM. Please reply Yes or No.
- Service excellence and recovery—BA will track the service it provides to its customers and aim to keep it at a high level. Given air travel constant problems and disruptions, BA wants to understand what problems its customers experience, and do its best to recover a positive overall result
- Offers that inspire and motivate—BA’s best customers are business travelers who don’t have time for irrelevant offers, so Know Me program analyzes customer data to construct relevant and targeted “next best offers” for their consideration.
The information to support these objectives is integrated across a variety of systems, and applied in real-time customer interactions at check-in locations and lounges. Even on BA planes, service personnel have iPads that display customer situations and authorized offers. Some aspects of the Know Me program have already been rolled out, while others are still under development.
The Need for New Data Roadmaps
New IT paradigms (cloud resident apps, mobile apps, multi-channel, always-on etc.) are creating more and more complex integration landscapes with live, “right-now” and real-time data. With data increasingly critical to business strategy, the problems of poor quality data, fragmentation, and lack of lineage are also taking center stage.
The big change taking place in the application landscape: application owners of the past expected to own their data. However, applications of the future will leverage data – a profound change that is driving the data-centric enterprise. The applications of the future need one “logical” place to go that provides the business view of the data to enable agile assembly.
Established and startup vendors are racing to fill this new information management void. The establish vendors are expanding on this current enterprise footprint by adding more features and capabilities. For example, the Oracle BI stack (hardware – databases – platform – prebuilt content) illustrates the data landscape changes taking place from hardware to mobile BI apps. Similar stack evolution is being followed by SAP AG, IBM, Teradata and others. The startup vendors typically are building around disruptive technology or niche point solutions.
To enable this future of information management, there are three clusters of “parallel” innovation waves: (1) technology/infrastructure centric; (2) business/problem centric; and (3) organizational innovation.
IBM summarize this wave of innovation in this Investor Day slide:
Data Infrastructure Innovation
- Data sources and integration — Where does the raw data come from?
- Data aggregation and virtualization- Where it stored and how is it retrieved?
- Clean high quality data — How does the raw data get processed in order to be useful?
Even in the technology/infrastructure centric side there are multiple paths of disruptive innovation that are taking along different technology stacks shown below.
- Data-as-a-Service: Data Provisioning, Management, Lineage, Quality
- Reporting-as-a-Service: Dashboards, KPIs, Drilldowns/Aggregates…. Descriptive
- Analytics-as-a-Service: Predictive Modeling and BI… Prescriptive analytics
- Information-as-a-Service: Threshold based Alerts, Exceptions, Mobile Prompts
- Insights-as-a-Service: ML/AI based…automated learning – ambient intelligence, Next best Offer/Action
Which strategy are you implementing?
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.
So, if you are a senior leader in a Fortune 2000 company. How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure, toolset, dataset, skillset and mindset for analytics and Big Data?
Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations. In 2012, business users are exerting significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).
Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?
According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.
While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.
Here are just a few examples of analytics at work
- Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
- Tesco (UK) annually issues 100 million personalized coupons at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
- Netflix predicts which movies you will like based on what you watched.
- Life insurance companies can predicts the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
- Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.
Now you are interested. So what about your organization. Do you have the right toolset, dataset, skillset and mindset for analytics? Do you want to enable end users to get access to their data without having to go through intermediaries?
The challenge facing managers in every industry is not trivial… how do you effectively derive insights from the deluge of data? How do you structure and execute analytics programs (Infrastructure + Applications + Business Insights) with limited budgets?
Data-driven DNA is about having the right toolset, mindset, skillset and dataset to evolve a major brand and seize today’s omni-channel opportunities. Whether it’s retooling and retraining for the multiscreen attention economy, or introducing digital innovations that transform both retail and healthcare, P&G is bringing data into every part of its core strategies to fight for the customer.
Striving for market leadership in consumer products is a non-stop managerial quest. In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment.
CMOs and CIOs everywhere agree that analytics is essential to sales & marketing and that its primary purpose is to gain access to customer insight and intelligence along the market funnel – awareness, consideration, preference, purchase and loyalty.
In this posting we illustrate a best-in-class “run-the-business” with Data/Analytics Case Study at P&G. The case study demonstrates four key characteristics of data market leaders:
- A shared belief that data is a core asset that can be used to enhance operations, customer service, marketing and strategy
- More effective leverage of more data – corporate, product, channel, and customer – for faster results
Technology is only a tool, it is not the answer..!
- Support for analytics by senior managers who embrace new ideas and are willing to shift power and resources to those who make data-driven decisions
This case study of a novel construct called Business Cockpit (also called LaunchTower in the Biotech and Pharmaceutical Industry) illustrates the way Business Analytics is becoming more central in retail and CPG decision making.
Here is a quick summary of P&G Analytics program:
- Primary focus on improving management decisions at scale – did the analysis to identify time gap between information and application to decision making
- “Information and Decision Solutions” (IT) embeds over 300 analysts in leadership teams
- Over 50 “Business Suites” for executive information viewing and decision-making
- “Decision cockpits” on 50,000 desktops
- 35% of marketing budget on digital
- Real-time social media sentiment analysis for “Consumer Pulse”
- Focused on how to best apply and visualize information instead of discussion/debate about validity of data
The exploding demand for analytics professionals has exceeded all expectations, and is driven by the Big Data tidal wave. Big data is a term commonly applied to large data sets where volume, variety, velocity, or multi-structured data complexity are beyond the ability of commonly used software tools to efficiently capture, manage, and process.
To get value from big data, ‘quants’ or data scientists are becoming analytic innovators who create tremendous business value within an organization, quickly exploring and uncovering game-changing insights from vast volumes of data, as opposed to merely accessing transactional data for operational reporting.
This EMC infographic summarizing their Data Scientist study supports my hypothesis – Data is becoming new oil and we need a new category of professionals to handle the downstream and upstream aspects of drilling, refining and distribution. Data is one of the most valuable assets within an organization. With business process automation, the amount of data being generated, stored and analyzed by organizations is exploding.
Following up on our previous blog post – Are you one of these — Data Scientist, Analytics Guru, Math Geek or Quant Jock? — I am convinced that future jobs are going to be centered around “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” data chain. We are simply industrializing the chain as machines/automation takes over the lower end of the spectrum. Also Web 2.0 and Social Media are creating an interesting data feedback loop – users contribute to the products they use via likes, comments, etc.
CIOs are faced with the daunting task of unlocking the value of their data efficiently in the time-frame required to make accurate decisions. To support the CIOs, companies like IBM are attempting to become a one-stop shop by a rapid-fire $14 Bln plus acquisition strategy: Cognos, Netezza, SPSS, ILog, Solid, CoreMetrics, Algorithmics, Unica, Datacap, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail). IBM also has other information management assets like Ascential, Filenet, Watson, DB2 etc. They are building a formidable ecosystem around data. They see this as a $20Bln per year opportunity in managing the data, understanding the data and then acting on the data. Read more
Fidelity Investments put out an interesting analysis on Big Data as a Macro Investment Themes for clients. Since everyone has an underperforming investment portfolio in this current market, I reproduced the article here to generate some ideas.
- New types of large data sets have emerged because of advances in technology, including mobile computing, and these data are being examined to generate new revenue streams.
- More traditional types of business data have also expanded exponentially, and companies increasingly want and need to analyze this information visually and in real time.
- Big data will be driven by providers of Internet media platforms, data amalgamation applications, and integrated business software and hardware systems.
Investment Theme – Big Data
The concept of “big data” generally refers to two concurrent developments. First, the pace of data accumulation has accelerated as a wider array of devices collect a variety of information about more activities: website clicks, online transactions, social media posts, and even high-definition surveillance videos.
A key driver of this flood of information has been the proliferation of mobile computing devices, such as smartphones and tablets. Mobile data alone are expected to grow at a cumulative annualized rate of 92% between 2010 and 2015 (see Exhibit 1, below). Read more