Monthly Archives: September 2014

Five Hot Trends Impacting Your Decision-Making Environment and What You Need to Worry about

All, in today’s TDWI Webinar, Claudia Imhoff gave a talk on the five hot BI trends impacting Data Management. They are Big Data Analytics, Advanced Analytics, Self-service BI, BI Mobile Device and Cloud-based BI Solutions.

A few take-away about Big Data Analytics are:

Definition of this trend: Data sets with sizes beyond ability of commonly-used software tools to capture, integrate, manage, and process within a reasonable amount of time.

Impact: Hence the rise of Hadoop, data warehouse appliance with solid state drives or in-memory technologies and data virtualization.

One-size fits all data management is no longer viable due to the workload changes placing on the DWH architecture. Therefore we will need to

Extend DW environment to support new workloads, for example add Hadoop into the architecture that can be integrated with existing data warehouse

Need to modify data modeling and integration approaches such as include data virtualization, data blending and *data refineries

Modify data governance approaches – use different levels of governance on security, compliance, quality and retention needs

What are *data refineries? Data lakes such as Hadoop technology may need to be used as sand box and experimental areas for data refineries.

Combining consumer data to solve big problems

This Forbes article includes some great examples of business models evolving to make money and improve our lives through mining and sharing data. The businesses are not making money immediately but are investing in data capture, analytics and partnerships to start capitalizing on their data for long term revenue.

Key points in the article:
- Your data combined with those of thousands of other people can tackle bigger problems such as cutting your company’s health care budget or sparing the nearby utility from building another power plant.
- Smart-thermostat maker Nest Labs has quietly built a side business managing the energy consumption of a slice of its customers on behalf of electric companies.
- In wearables, health tracker Fitbit is selling companies the tracking bracelets and analytics services to better manage their health care budgets, and its rival Jawbone may be preparing to do the same.

- These companies are capitalizing on the terabytes of data they collect from consumers and, to an extent, on the largesse of taxpayers. State governments have increased the money–from $1.3 billion in 2003 to $6 billion in 2012–allocated to helping utilities manage energy demand, according to the U.S. Energy Information Administration.

See full article -

How can we leverage our data either directly or with business partners/customers to generate revenue and solve big problems?

Start-ups are now offering BI solutions to our middle market customers

We know we have data which we can leverage as an asset. We also can derive great insights or predictions for our customers. In this article it describes what start-ups are doing to build a winning BI business model in this middle market. I like the intercept/middleware model mentioned near the end of the article. CDF is looking at potentially playing in that space or partnering with other companies that offer services to dealers and capture additional data from the dealers.

Great analysis of the 2014 Gartner BI Quadrant – winners and losers

The Gartner report was released a few months ago. I was looking for an objective review of their quadrant and who was gaining ground and who was losing ground. I found this excellent summary with insightful commentary. Link

Gainers: Tableau, Qlik and Spotfire. Losers: Microsoft, MicroStrategy, SAP and Oracle.

We don’t need more tools right now but we do need to keep an eye on the future for when we might want to retire a tool or move more solutions to a tool we already have.

Hope you find the analysis informative

The Industries Plagued by the Most Uncertainty

It’s a cliché to say that the world is more uncertain than ever before, but few realize just how much uncertainty has increased over the past 50 years. To illustrate this, consider that patent applications in the U.S. have increased by 6x (from 100k to 600k annually) and, worldwide, start-ups have increased from 10 million to almost 100 million per year.  That means new technologies and new competitors are hitting the market at an unprecedented rate.  Although uncertainty is accelerating, it isn’t affecting all industries the same way. That’s because there are two primary types of uncertainty — demand uncertainty (will customers buy your product?) and technological uncertainty (can we make a desirable solution?) — and how much uncertainty your industry faces depends on the interaction of the two.

Demand uncertainty arises from the unknowns associated with solving any problem, such as hidden customer preferences. The more unknowns there are about customer preferences, the greater the demand uncertainty. For example, when Rent the Runway founder Jenn Hyman came up with the idea to rent designer dresses over the internet, demand uncertainty was high because no one else was offering this type of service.  In contrast, when Samsung and Sony were deciding whether to launch LED TVs, which offered better picture quality than plasma TVs at a slightly higher price, there was lower uncertainty about demand because customers were already buying TVs.

Technological uncertainty results from unknowns regarding the technologies that might emerge or be combined to create a new solution. For example, a wide variety of clean technologies (including wind, solar, and hydrogen) are vying to power vehicles and cities at the same time that a wide variety of medical technologies (chemical, biotechnological, genomic, and robotic) are being developed to treat diseases. As the overall rate of invention across industries increases, so does technological uncertainty.

Consider the 2×2 matrix below. The horizontal axis plots each industry based on technological uncertainty, measured as the average R&D expenditures as a percentage of sales in the industry over the past ten years. The vertical axis plots each industry’s demand uncertainty, measured as an equal weighting of industry revenue volatility, or change, over the past 10 years and percentage of firms in the industry that entered or exited during that same time period. Although these are imperfect measures, they identify the industries facing the highest, and lowest baseline levels of uncertainty.

Interesting blog entry on how Big Data and Data Warehousing are coming together

Big Data is not making the Data Warehouse obsolete overnight. The apostles of the Data Warehouse have fought back and demonstrated that it is not always as simple as “load and go.” Although some data engineering has been eliminated or reduced, and Big Data approaches are reducing the costs of data management, data still needs to be standardized, data quality maintained, and access provided to constituent communities. Data management will continue to be an evolutionary process.