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Posts tagged ‘Business Intelligence’

27
Apr

Chief Data Officer Role & Challenges


Chief Data Officers, Chief Analytics Officers,  Chief Data Science Officers and Chief Digital Officers are showing up everywhere. The job is to leverage the latest in predictive analytics, data science, machine learning, and multi-tenant cloud architecture to bring innovation to traditional processes.

This is a pivotal moment in data driven business models but there is no getting around the inherent difficulties associated with either altering organizational behavior, data ownership politics or managing wholescale transformation of the data infrastructure. And while the challenges are real, many firms are getting closer to achieving a data science and data management environment.

What are data and analytics officers overseeing… A variety of foundational & plumbing strategies:

  • 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 “Systems that Learn”…automated learning – ambient intelligence, Next best Offer/Action

At core of all these, Data Management and Data Science tools are core technical and business capabilities.  Some firms are more mature and further along than others.

Why Mature Data Management as a Function

Organizations live or die by the quality of their data.

Data is an underlying factor of input into business operations and essential in order to  facilitate process automation, digitize operations, support financial engineering and enhance customer facing analytical capabilities.

An effective data management program requires a planned strategic effort

  • Integrate multi-discipline efforts
  • Inculcate a shared vision and understanding
  • Data is a ‘thing’ – vital infrastructure element foundation of the n-tier architecture
  • Not a project, more than a program…it’s part of the core foundation

There is no question about it – the foundational levels of people, process, governance and technology required to establish data management on a  sustainable basis are coming together under the CDO umbrella.

What does a Chief Data Officer (CDO) do?

Read more »

24
Feb

Security Analytics – Big Data Use Case


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.

securityanalytics3

 

Here are three recent examples that I was personally affected by – Forbes,  Target, Neiman Marcus.  

Read more »

23
Oct

Market Sizing – Business Analytics and Big Data


future-of“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.

WorldofDataChanging

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.

Read more »

15
Jul

Informatics or Analytics? Understanding Digital Health Use Cases


Healthcare Provider Value Chain

Every firm wants to bring the power of big data and data science to streamline healthcare encounters ~ member/consumer engagement, provider/PCP engagement or clinical/care engagement.

————-

Health expenditures in the United States crossed $3.0 trillion in 2013 which is more than ten times the $256 billion spent in 1980.

Almost 15% of U.S GDP is spent on healthcare…a staggering number.  As a mega-vertical, healthcare covers several major segments (the 7 Ps)

  • Payers (Health Insurance and Health Plans),
  • Providers (Hospital Systems, Labs and IDNs),
  • Pharmacy (retail distribution networks), and
  • Pharmaceutical and medical equipment manufacturers,
  • Prescribers (Physicians, clinics and pharmacy minute clinics)
  • Police (Regulators, FDA)
  • Patients (consumers)

U.S. healthcare system is a complex beast and difficult to navigate – providers need to make it easier for patients. They are using people resources like care coordinators and patient navigators to help patients navigate the system.

The focus on the payor side is in digitizing health today is to reduce the amount of waste in the health care system via implementation of new forms of health IT and Analytics… that reduces inefficiencies, redundancies and administrative costs.

According the CEO of Aetna…”the health care system wastes more than $765 billion each year – that’s 30 percent of our health care spending.”

AgingPopulationWhile spending on health care is dominating headlines, the health care industry (7Ps) is in a state of flux. Stakeholders across the health care sector are running hard to reduce costs.   The drivers impacting cost of healthcare include:

  • Aging population – Patient history and patterns of care impacting patient readmission rates
  • Rise in Chronic Disease – 75% of cost – Prevention not reactive medicine
  • Drug cost – escalating for certain therapies (Generics exchanged for biological drugs)

The healthcare ecosystem is being reshaped by two powerful counter economic forces at work:  (1) Improve quality of care  and   (2) drive the cost of care down. Basically spend less and get more.

As a result,  the entire healthcare  ecosystem is changing into a “information-driven”, “evidence-based” and “outcome-driven” model.

The target healthcare transformation goals are:

  • align economic incentives between payers and providers,
  • digital engagement…create a simpler, more transparent consumer experience, and
  • connected health….technologies that seamlessly connect our healthcare system.

In this posting we look at Digital Health Care use cases and how data and analytics are being slowly but sure being adopted in the form of informatics. All this change is being driven under the guise of Health Reform.

Read more »

23
May

Data Monetization: Turning Data into $$$


DataExplosionThe billion dollar question facing executives everywhere:

  • 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. 

Read more »

15
Jan

Big Data Fatigue and Company Shakeout?


hype cycleBig Data is the latest “next big thing” transforming all areas of business, but amid the hype, there remains confusion about what it all means and how to create business value.

Usually when there is so much hype…there is an inevitable boom-bust-boom cycle. Hence my question:  Is the Big Data shakeout inevitable?

Are we in a big data tech bubble? If you are an enterprise customer, how do you prepare for this? What strategies do you adopt to take advantage of the situation? Can you move from lab experiments to production deployments with confidence?

The sheer number of companies that are chasing “the pot of big data gold” is astounding (see below).  While the innovation has accelerated the ability of the typical Fortune 1000 enterprise to absorb and assimilate has not. They tend to be 5-10 years behind the curve. As a result, many big data startups are either running out of cash or they are being folded by VCs into other firms.  This boom-bust cycle is a typical pattern in innovation.

http://www.bigdata-startups.com/open-source-tools/

BigDataUniverse

Source: Big Data Universe v3.. Matt Turck, Sutian Dong & FirstMark Capital

The Case of Drawn to Scale

Drawn to Scale, the four year-old startup behind Spire, shut down recently. Co-founder and CEO Bradford Stephens announced the news in a blog post. Drawn to Scale raised .93M in seed funding.

Spire is a real-time database solution for HBase that lets data scientists query Hadoop clusters using SQL. According to Stephens, the system has been by deployed by American Express, Orange Flurry, and four other companies.

Drawn to Scale showed that its technology was viable in enterprise environments and established a “presence against  competitors who raised 10-100x more cash,” but even that wasn’t enough to save the startup from its financial woes.

As Hadoop evolves and different layers of the data analytics stack get commoditized, specialized vendors like Drawn to Scale will have problems surviving.   SQL-on-Hadoop was a unique feature set…but over time it has become a must-have feature, that is becoming embedded in the stack – e.g., Impala in Cloudera CDH stack.  As a result, firms like Drawn to Scale once unique functionality becomes difficult to monetize.

Startup to Viable Ventures

The Big Data ecosystem is exploding with exciting start-ups, new divisions and new initiatives from established vendors.  Everyone wants to be the vendor/platform of choice in assisting firms deal with the data deluge (Data growth curve: Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes), translate data to information to insight, etc.

In both U.S and Europe, several billion dollars of venture money has been invested in the past three years alone in over 300+ firms.  Firms like Splunk had spectacular IPOs. Others like Cloudera and MapR have raised gobs of money. In the MongoDB space alone – a small market of less than 100M total revenue right now, over $2 Billion is said to have been invested in the past few years.

Read more »

20
Aug

Innovation and Big Data: A Roadmap


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:

datadrivers

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.  

Read more »

19
Jun

Organizing for BI, Analytics and Big Data: CoE, Federated or Departmental


  • 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).

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9
Apr

Bloomberg on Business Analytics


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.

Read more »

8
Mar

Spend BI and Analytics


What is your spend IQ or data maturity?

Procurement organizations tend to swim in data. One of the most important strategies for any best-in class procurement organization is spend analytics. In conjunction with sourcing, category, contract management and purchasing, spend analytics provides a window into spend behavior to drive cost reduction and cost avoidance efforts.

As a result, we are seeing a lot of interest around Spend BI and Analytics projects. Chief Procurement Officers and other Sourcing/Procurement leaders of Global, large and even mid-market firms are increasingly focusing on spend data analytics as part of a new wave of spend rationalization projects. Read more »

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