#19 Big Data beyond the hype

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The Big Data hype cycle is in full swing. But what is Big Data? How do you know if your data is BIG?

Big Data is not a concretely definable category. You can’t always say exactly what it is, but you know it when you see it. In this episode I define the key characteristics of Big Data that enable us to make more intelligent assessments and decisions regarding Big Data solutions.

Key characteristics of Big Data.

  • Physical Attributes

    • Bigness: physical size of data sets

    • Multi-source: data from multiple sources, especially both internal and external to the organization

    • Multi-structure: tabular data, markup data, audio and video data, geospatial, activity, transactions, snapshots, statuses

    • Fast arriving: streaming, frequently updated, time volatile

  • How we process it

    • Real time analysis

    • Real time outputs

      • Delivery to decision makers in real time

      • Delivery to external users (consumers, social/mobile users)

      • Interaction with software APIs

    • Aggregate and details

  • What we do with it

    • Predictive value

    • Pattern recognition, especially unlikely relationships

      • fuzzy matching

      • flexible matching

  • Challenges

    • Storage

    • Processing

    • Integration

    • Analysis

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#18: BI Trends for 2013 and beyond

In this episode I talk about “BI Trends for 2013 and beyond.”
Check out the video on the Northwood Advisors web site.
Here is the link to my article: Rising to the Challenges in BI Healthcare
The 4 trends – listen to the audio to get all the details:
1. The maturing of broad BI platforms
For most companies, they can meet most of the needs of most of the people most of the time.
2. Niche tools fill the gaps
Analysts benefit from powerful tools from best-of-breed niche tools across a variety of functions.
3. Mobile BI
Putting usable content in the hands of users in the places it’s most needed is a reality that is here today and won’t be going away.
4. Big data pushing the envelope
Despite the confusing hype, the need is real and there is a growing array of tools to solve the problem.
Thanks for listening to the BI Podcast that focuses on creating a better world through better decisions.

17 Why BI Matters

Previous episodes of the Real Time Decisions Webcast have covered a lot of material about What is BI and How to do BI, but haven’t dug too deeply into Why BI Matters. In this episode, Myron Weber explores this important topic, providing information and inspiration for your company’s BI efforts.
Check out my company web site at www.NorthwoodAdvisors.com
Also, as recommended in the podcast, check out my business coach, Dave Luke at www.DaveLukeAdvisory.com
Thanks for listening to the BI Podcast that strives to change the world with better decisions.

#16 Real World BI Requirements

In this episode of the webcast, I discuss Real World BI Requirements. 
BI Requirements in the real world has to avoid two common unconstructive extremes and provide a constructive alternative.
The first extreme is a data-driven approach that fails to account for the objectives and outputs required.
The other extreme is a blank-page approach.
The audio explores these concepts and provides a constructive alternative – check it out and share your thoughts in the comments.
Don’t forget to check out my company at www.northwoodadvisors.com to learn about our advisory services for BI Strategy, Roadmap, Governance, and Best Practices.

Analysts: Stop Being Evil!

Ever notice how some business functions get more respect inside their own companies than others? It’s a natural part of business life: all roles are not perceived equally.

Sales is always a good candidate for front-of-the-line, top-shelf, Grade-A respect – partly because sales is truly an important function, and also in many cases because the sales team is so prone to stepping up and claiming that spot. Other perennial front runners in the respect category, depending on the industry, can include manufacturing (because, after all, they make the stuff), operations (they keep things running), and professional services (where would the organization be without the fees of its doctors, lawyers, engineers, or consultants).

Other functions’ respect can be more hit-and-miss. In some companies, marketing rules the roost – owning the brand and strategic direction – while in others, the marketing folks are relegated to maintaining the official PowerPoint template. The customer service call center can empowered to be great (Zappos anyone?) or… well, don’t get me started on Bank of America. HR can perceived as the key to attracting and retaining the talent that gives you an edge, or they can be viewed as the annoying pencil-pushers that you have to keep around. And there are companies that are built on the back of their technology capabilities and give the nerds a great deal of respect, while others would fire the whole IT team and go back to pencils and postage stamps if they could.

Some business functions and roles are viewed as contributing to success.

And some are considered a necessary evil.

It is in this context that I urge Analysts: Stop Being Evil! Okay, what I really mean is, stop allowing your role to be perceived as a necessary evil.

Over the past several years the role of the Analyst in business has risen in prominence, with an increasing flow of books, articles, software, conferences, and buzzwords directed at the field of Analytics. This is a positive trend in my view, and a substantive one that reflects the convergence of new competitive strategies based on management science with enterprise software that has the capability to make Analytics mainstream. But let’s not kid ourselves into thinking this is entirely new – there are industries (especially in the finance & insurance sector) that have been built on analytics for decades. In those companies, analysts get respect. And the shining stars of the case studies and books (Harrah’s, the Boston Red Sox, etc.) achieved success by elevating analytics to be a competitive differentiator.

But then there is the rest of the world. As I advise companies on strategy and execution for BI & Decision Systems, I see the sad, mainstream reality of Analysts who are treated as a necessary evil. This is reflected in the chronic late nights and lost weekends that happen not because the Analyst is on the cusp of a breakthrough insight, but because of the grueling spreadsheet march it takes to produce marginally useful “required metrics” without adequate systems and training. The Analyst is necessary because the business has grown to believe that it needs those spreadsheets to monitor its performance. But the Analyst is a necessary evil in the sense that the role is seen not as strategic, not as a competitive advantage, nor as a driver of business change, but rather as a cost of doing business – one they would eliminate if they could.

So what to do about this situation?

First, I think many Analysts need to aspire to greater things. Something beyond data crunching. Beyond delivering metrics and KPIs. Even beyond stats and operations research. A worthy goal for the Analytics function, I believe, is to own and drive better decision-making processes across the organization.

Second, I think some Analysts themselves need to consider their part in this situation. If you fancy yourself an Analyst, and yet the only analysis software you know how to use is Microsoft Excel, you are part of the problem. Train up or change your title.

Third, I encourage Analysts to be selective – when you take a job (or keep the one you’ve got), if it’s not clear that Analysts are viewed as providing true value, be wary. Get specific commitments about the Analyst role, about the investment to support Analytics, and the importance of the role within the organization.

The Analyst role can be a force for good, for transformation, and for the betterment of mankind – the motto of this webcast is “Better Decisions Lead to a Better Life.” Don’t let Analytics be seen as a necessary evil. Please.

Myron Weber is Managing Partner at Northwood Advisors.

This article was inspired when some great folks at the Smart Data Collective invited me to participate in an Analytics Blogarama on the topic of “The Emerging Role of the Analyst.” Check out the rest of the entries at http://smartdatacollective.com/40832/analytics-blogarama-october-6-2011.

#14 Healthcare Data Warehousing Challenges

Check out our newest sponsor Northwood Advisors, premier advisory services for BI Strategy & Execution.
I have worked for quite a few healthcare companies over the past few years, and I’ve found some common threads of key challenges and some approaches to addressing those challenges. Check this episode out even if you don’t work in that field and let me know whether you agree or disagree with my thoughts.
Key Goals In Healthcare Decision Systems
  1. Improving Patient Outcomes
  2. Optimizing Provision of Service
  3. Management of Cost of Service
Key Data Entities
  1. Patient
  2. Provider
  3. Visit or Encounter
    1. Symptoms
    2. Diagnoses
    3. Procedures
    4. Test Results, Labs, Indicators
    5. Prescriptions
    6. Revenue and Cost
  4. Patient Medical History
  5. Payer
    1. Payment plans, schedules
    2. Patient Eligibility
  1. Security – data and content level security
  2. Data Quality – healthcare on average seems worse than other industries, partly due to source systems that do a poor job and also due to unstructured, non-quantitative data
  3. Retroactive changes to data
  4. Complex many-to-many relationships (e.g. multiple diagnoses and procedures in a single encounter)
  5. Complex set and subset analysis
  6. Lack of technology and focus on Business Systems
Mitigation Strategies
  1. Executive Sponsorship: fostered by clear BI Strategy and Roadmap
  2. Outputs Analysis to develop requirements based on the decisions and business questions
  3. Data Quality Strategy: resolve at the source system when possible, use appropriate tools, define process & ownership
  4. Using Design Patterns (see episode 13)
  5. Focus on the right tools capabilities, especially for complex set analysis

#13 Design Patterns in Data Warehousing

Show Notes

In this episode I discuss the concept of Design Patterns in Data Warehousing and present 4 examples:

  • Operational
    • Inheritance
    • Statuses
  • Financial
    • Currency
    • Transfer Pricing

Overall, I feel like this audio podcast is one of my least effective in that I found the dense technical concepts more difficult to articulate clearly than I have in other shows. It makes perfect sense in my head, but is difficult to put into words concisely. To make up for it, the show notes are more robust than usual and I feel good about posting the podcast plus notes. I hope you find it helpful.