Skip to content

Posts tagged ‘Data warehouse’

25
May

Big Data Analytics Use Cases


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)?

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 »

3
May

Executing a BI and Analytics CoE


Most Organizations are Data Rich and  Information Poor

——————————

Data overload is becoming a huge challenge for businesses and a headache for decision makers.  Public and private sector corporations are drowning in data — from sales, transactions, pricing, supply chains, discounts, product, customer process, projects, RFID smart tags, tracking of shipments, as well as e-mail, Web traffic and social media.

I see this data problem getting worse. Enterprise software, Web and mobile technologies are more than doubling the quantity of business data every year, and the pace is quickening. But the data/information tsunami is also an enormous opportunity if and only if tamed by the right organization structure, processes, people and platforms.

A BI CoE (also called BI Shared Services or BI Competency Centers) is all about enabling this disciplined transformation along the information value chain:  “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”  A BI CoE can improve operating efficiencies by eliminating duplication and streamlining processes.

In this posting we are going to look at several aspects of executing a BI CoE:

  • What does a BI CoE need to do?
  • Insource or Outsourcing the BI CoE
  • Why do BI CoE’s Fail?
  • BI CoE Implementation Checklist

Read more »

2
May

BI, Analytics, Reporting Center of Excellence (CoE)


Everyone has data, but the more elusive goal is getting value out of that data  The growing challenge in corporations is how to organize for “data as a platform.” What is the right organizational structure that will help monetize data?

John Wanamaker, considered a pioneer in modern advertising, said: “Half the money I spend on advertising is wasted; the problem is I don’t know which half.” Today, we can say the same of enterprise investment in business intelligence (BI), analytics, and big data.

Even after doing their best for over 20 years to build centralized, scalable information architecture, I found that only a small percentage of organizations’ data is actually converted to useful information in time to leverage it for better insight and decisions.

At both strategic and tactical levels, much of this gap can be explained by the fundamental disconnect in goals, objectives, priorities, and methods between IT professionals and the business users they should ideally serve.

The other challenge facing leadership is the rapid evolution of the data platform (see below.)  How do you create strategies that adapt to a changing landscape?

Evolution of Data Platform

Leadership Challenge

How do you become a world-class data-driven firm? What portfolio of projects do you execute to mature the capabilities?

If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your BI, Reporting or Analytics initiative. While the nuances – skillsets, toolsets and datasets — are different for each initiative, the fundamentals of managing, organizing and structuring are pretty much the same.

Almost every Fortune 1000 company’s management is increasingly focused on monetizing small data, big data or fast data, and how to gain a real-time competitive edge from their information. How can firms achieve positive returns on their analytic investments by taking advantage of the growing amounts of data?

So what’s the right organizational model that will help them achieve the “ten second advantage”? Competency Centers, Centers of excellence (CoE) or Shared Services models are execution models to enable the corporate or strategic vision to create an enterprise that uses data and analytics for business value.

BI CoE Slide

The goal of every World-class CoE is the same – enable the right combination of toolsets, skillsets, mindsets and datasets for better, faster, cheaper and more repeatable analytics, reporting or platform development.

Evolution of BI/Reporting/Analytics

  • Data is Growing Faster than Budgets
  • Demand is Growing, Speed to Insight is Crucial
  • Modifying large, existing applications is NOT the path forward.
  • Skills are lagging.. New tooling

As a result, Enterprise BI and Analytics strategies need to evolve.  The evolution tends to happen in 3 phases:

  • Department Solutions – Many companies deploy Analytics (and BI) applications as departmental solutions, and in the process, accumulate a large collection of disparate BI technologies – SAP Business Objects, IBM Cognos, Microstrategy, Oracle OBIEE, Microsoft, Qlikview, Tableau, Spotfire etc. – as a result. Each distinct technology supported a specific user population and database, within a well-defined “island of analytics.” At first, these dept islands satisfied the initial needs of the business, but early success in departmental deployment sowed the seeds for new problems as the applications grew.
  • Successful applications and platforms always expand. The second phase of Analytics (and BI) is where there is tremendous growth and  platform solutions are longer isolated islands. Instead, they overlap in user populations, data access, and analytic coverage. As a result, organizations are now faced with an untenable situation. The enterprise is getting conflicting versions of the truth through the multiple disparate BI systems, and there is no way to harmonize them without an extraordinary ongoing manual effort of synchronization, validation and quality checks. Equally problematic is the fact that business users are forced to use many different BI tools depending on what data they want.
  • The third phase of Analytics (and BI)  is one where the executives had enough. They simply make a decision to rationalize to a single platform or a centralized model that is sold as a “magic nirvana” solution…delivers one version of the truth (golden source of data) to all people across the enterprise. It can access all of the data, administer all of the people, eliminate repetitive data access, reduce the administrative effort, and reduce the time to deploy new BI applications.

“Time to decisions, scope of decisions, disconnected toolsets and cost of decisions” is deemed unacceptable within & across functional areas.  This typically drives a new phase… centralized BI, Reporting or Analytics CoE.

For example, at a Fortune 500 company, costly self-service environment, static reports, departmental solutions and other issues (shown below) forced them to re-think and re-engineer their enterprise BI solution. The firm set new target objectives…(1) Shorter time to insights; (2) Greater leverage for analytics team; (3) Accelerated product innovation and (4) 20% reduction in BI support costs.

While centralization of BI, Reporting and Analytics can enable organizations to reduce their IT delivery costs by up to 40%. However, a failure to align the level of BI, Reporting and Analytics centralization closely to long-term business and IT strategic goals and to manage the transition to centralized delivery carefully can not only erode expected savings from centralization, it can increase the cost of delivering IT services by up to 30-45% compared to a pre-centralization baseline.  This where good management can make a big difference.

BusinessChallengeFortune500

BI CoE Elements for Faster, Better, Cheaper Execution

BI CoE (could be Analytics CoE,  Big Data CoE or Integration CoE) is an organizing mechanism to align People,  Process,  Technology and  Culture.  The target benefits include:

  • Better collaboration between Business and IT
  • Increased adoption and use of BI and Analytics in the lines of business.
  • Better data management, quality and reporting
  • Cost savings from eliminating redundant functions

CoE elements include:

ElementsofCoE

Read more »

%d bloggers like this: