E-Book
Developing a business analytics
strategy: Key considerations and
expert advice
Business analytics offers a host of promised benefits – from improved operational efficiency to increased sales to a better understanding of customers. But developing a strategy and structure to deploy business analytics successfully can be challenging. Which projects should be prioritized? How should the team be structured for success? What technology innovations are key to include in your strategy? In this eBook, appropriate for both business and IT professionals, learn more about developing a successful, scalable business analytics strategy.
E-Book
Developing a business analytics
strategy: Key considerations and
expert advice
Table of Contents
Deploying data analytics tools requires focus on
more than technology
By Todd Morrison, News and Features Editor
Technical issues are only part of the equation when it comes to deploying advanced business and data analytics software, according to industry analysts.
Quite often, whether an analytics project succeeds or fails depends on human factors – for example, whether an organization‟s IT department collaborates sufficiently with the
business users who will be relying on the data analytics tools, and whether those users feel comfortable with the software once it‟s up and running.
“You can‟t deploy these technologies without considering people and process issues,” said Gartner Inc. analyst Rita Sallam. In fact, meeting the needs of users is more important than the particular features and capabilities of whatever analytics technology is chosen for a project, Sallam noted.
“To me, the technology itself is almost irrelevant – it‟s almost a red herring,” she said. “Business users couldn‟t care less about in-memory analytics. What they care about is being able to rapidly and intuitively analyze large amounts of data.”
Buying and installing a particular kind of business analytics software simply because it‟s the latest and greatest technology on the market is a recipe for failure, Sallam added. To avoid problems, she advised, advanced data analytics tools have to be deployed in close
partnership with end users.
Keep the lines of communication open on data analytics tools projects Howard Dresner, a former Gartner analyst who now is president of Dresner Advisory
Services LLC in Nashua, N.H., agreed with Sallam. IT departments and business intelligence (BI) teams that support deployments of analytic applications must communicate closely and openly with business users as technology decisions are made, he said.
“IT needs to be responsive. They need to be joined at the hip with the end user,” said Dresner, who coined the term “business intelligence” while working for Gartner.
Of course, business analytics tools must also meet an organization‟s overall needs and requirements to justify investments in them – a fact that isn‟t lost on Sri Vemparala,
manager of reporting and BI at Stanford University in Palo Alto, Calif. New technologies are always alluring – but Vemparala said that at Stanford, where the BI group supports the university‟s admissions, research and finance operations, in-memory analytics and other advanced data analytics tools aren‟t really needed yet.
“I would say 80% of our BI is operational reporting at this point,” Vemparala said, adding that technologies such as in-memory analytics would be “a step beyond that.” And while Vemparala is interested in exploring ideas for taking Stanford‟s BI program to the next level via analytics and performance metrics, he‟s only looking for now.
He said that likely would change only if an analytics software vendor proves to him that a technology could provide significant value to the university and be easily deployed – by adding a bundled appliance, for example.
Success with data analytics tools requires proper data management
In addition to ensuring that business analytics technology is the right fit for specific end users and an organization as a whole, IT and BI teams need to make sure that they‟ve fully addressed data management issues before deployment. In the case of in-memory analytics, data governance policies need to be put in place in order to ensure that data definitions, dimensions and calculations are consistent, Sallam said.
Proper change management procedures are also critical, from both a technology and
into training and supporting end users so that an analytics investment pays off in terms of adoption, usage and business results.
Sallam cited a 2006 Gartner case study on a project at Euro Disney, which operates the Disneyland Paris theme park, as an example of how an organization was able to successfully deploy BI and data analytics tools due to effective change management. She said the BI system was designed to predict and then monitor the length of lines at the park‟s rides and restaurants; when problems were identified, more workers were sent to the affected
locations, helping to boost customer satisfaction, according to Sallam.
Instead of trying to make those staffing decisions based on past experience or managerial intuition, park administrators learned to trust what the BI and analytics data was telling them – a shift in organizational culture that Sallam said Euro Disney was able to instill as part of the project.
Corporate BI standards could affect choices of data analytics tools
Businesses that have adopted a specific BI suite as a corporate standard should carefully consider the implications of buying data analytics technology that‟s outside of their
designated standard, according to Rick Sherman, founder of Stow, Mass.-based consulting firm Athena IT Solutions.
“If they do that, then they need to look at what the issues would be of having another technology and another data stack,” Sherman said, explaining that the need to coordinate data between different BI and data analytics tools could create complications for IT and end users alike.
Allowing ample time to make sure that all of the kinks have been worked out before any new technology is actually deployed sounds like simple advice – but it‟s something that many organizations overlook on BI and analytics projects, said Mark Smith, an analyst at Ventana Research in Pleasanton, Calif.
Creating an advanced data analytics business
culture: Tips and advice
By Craig Stedman, Site Editor, and Mark Brunelli, News Editor
The quickest, and potentially most successful, way to create an internal business culture that thrives on advanced data analytics technology and fact-based decision making is to start at the top of an organization, according to some IT professionals and industry analysts.
Just ask Bill Robinette, manager of business intelligence (BI) systems at Advance Auto Parts, a Roanoke, Va.-based retailer with about $5 billion in annual revenue. Two years ago, Robinette bore witness to the fact that a change in senior management can clear the way for the development of a data analytics business culture and a data warehousing, BI and advanced analytics program.
At a recent event held in Cambridge, Mass., by The Data Warehousing Institute (TDWI), Robinette said that when he joined Advance in 2006, business decisions were typically based on data stored in spreadsheets and Excel-generated cubes.
“Basically, we were running the business on gut feel,” he said, adding that more
sophisticated BI and analytics investments were a tough sell because company higher-ups were mainly focused on redesigning Advance‟s retail stores.
Going from ‘gut feel’ to an analytics business culture
Things changed in early 2008, when a former Best Buy executive took over as Advance‟s CEO and put a priority on improving the mix of parts in different stores based on local demand. Instead of the previous one-size-fits-all approach to merchandise planning, the company now uses data mining and predictive analytics tools to help automatically set plans for populating individual stores with parts, Robinette said.
Operational improvements enabled by the analytics tools have helped to solidify the
software‟s place in the company. For example, in the past, about 20% of the parts stocked in stores didn‟t sell within a year. Advance has used analytics to lower that figure to 4% -- a reduction that is “worth millions of dollars to our bottom line,” Robinette said. The company also uses performance metrics generated via its analytics applications to set growth targets for store managers and foster internal competition among stores.
Analysts say that Advance‟s experience with analytics technology is becoming more common these days: A technology-savvy CEO, often someone brought in to replace the previous top executive, pushes a company to use advanced data analytics software and methodologies to generate deep data insights that can support better business decisions. To help an analytics initiative succeed, senior executives need to drive an internal emphasis on optimizing business performance through quantitative measurements, TDWI analyst Wayne Eckerson said. They also have to put the company‟s money where their mouths are by funding and prioritizing analytics projects, he added.
Analytics software doesn’t equal an analytics business culture
But new analytics software and high-level executive support – while a good start – aren‟t enough to foster and maintain an analytics business culture. Companies also need to make sure that their employees have the ability to make the right decisions based on information gleaned from analytics technology, said Dan Vesset, an analyst at Framingham, Mass.-based IDC.
“I think that was part of the problem, for instance, with the financial crisis,” Vesset said. “The systems correctly identified risks, but the humans overrode those signals because they were incented to do so.”
He added that education and training are two of the keys to creating a long-lasting data analytics business culture. But that means more than simply teaching employees how to press buttons, click icons and read data on executive dashboards, he cautioned.
“We don‟t just mean training on the tools but also training on analytics techniques,” Vesset said. “There is a lack of people who are knowledgeable on the different ways of analyzing data.”
Employees should also be educated about the meaning of data as it pertains to their company‟s specific key performance indicators and performance metrics, he advised, while noting that such training is currently lacking at most companies.
Using an analytics group to help create an analytics business culture
Another potential way to help foster an analytics business culture within an organization is to set up a dedicated data analytics group, according to Eckerson, who put cultural issues at the top of a list of analytics challenges during a presentation at the TDWI event in
Cambridge.
While most companies haven‟t gone that far yet, he said, an analytics group with its own director could develop an analytics strategy and project plan, promote the use of analytics within the company, train data analysts on analytics tools and concepts, and work with the IT, BI and data warehousing teams on deployment projects.
One more point to keep in mind: Don‟t go overboard on the use of analytics tools. For example, Advance Auto Parts tied information gleaned from analytics software into a
performance dashboard application that was rolled out last year. The dashboard gives store employees a quick view of key performance metrics – a capability that Robinette said reinforces the importance and value of analytics without requiring front-line workers to delve deeply into it themselves.
Getting ready for an advanced business analytics
software project
By Mark Brunelli, News Editor
Companies looking to take advantage of business analytics software should first make sure that their data house is in order, according to industry analysts and other experts on analytics technology.
The first step in any analytics project is to get company data cleansed and profiled so that it can be made available for use by statisticians and other data analysts, according to Dr. Fern Halper, a partner at Hurwitz & Associates, a Needham, Mass.-based consulting and research firm that focuses on emerging software technologies.
“Data quality is always the most important thing, because „garbage in, garbage out,‟” Halper said. “That is something people have to understand.”
She added that one of the biggest data quality issues affecting organizations with business analytics software initiatives involves joining disparate data sources that may contain inconsistent information.
For example, a customer might be listed as “Customer A” in one data source, but the same listing in a different data source might be for its parent company. And perhaps “Customer A” in yet another data source could be a different company entirely. Without proper data cleansing and profiling, Halper said, the customer revenue totals calculated for the first customer would be way off, and analytical models incorporating the information would produce faulty results.
Eckerson cited data quality as one of the top-four analytics challenges during a presentation at a recent TDWI event in Cambridge, Mass. He recommended that IT and BI teams working on the projects consolidate detailed data in a single data warehouse, then integrate,
normalize and cleanse the information and standardize its underlying metadata before providing access to data analysts.
Overcoming the challenges of advanced business analytics software tools Many enterprises are also becoming more interested in advanced analytics technologies, such as predictive analytics software and tools that enable users to analyze voicemail messages, videos and unstructured text found in call center reports and corporate documents and on social media websites.
But the use of those tools requires careful preparation. For example, voice, video and text analytics technologies come with their own set of challenges to overcome, according to Thomas Davenport, co-author of the new book Analytics at Work and a professor of information technology and management at Babson College in Wellesley, Mass. Typically, the biggest problem that companies face when deploying unstructured data analysis tools is the fact that words can often have multiple meanings, Davenport said. And being able to discern what a specific word means in a particular usage requires business analytics software and systems to exhibit a human-like understanding of context and inflection.
For example, in a warranty report or on an invoice, the “buyer” could be an individual consumer, a worker within an organization‟s purchasing department or perhaps the
organization itself. Davenport said making sure that an analytics system can recognize and differentiate among the types of customers in those different scenarios may require some serious technical expertise and development work.
Halper agreed. The people who build analytical data models for an organization need “to be really attuned to understanding the ins and outs of data analysis,” she said. “It requires a deep understanding of your data and a certain thought process.”
Data gaps can hinder business analytics software
One of the toughest parts of large-scale analytics programs is integrating data from various departments and supply-chain partners, said Dr. Richard Hackathorn, founder of Bolder Technology Inc., a Boulder, Colo.-based consultancy specializing in analytics, BI and data warehousing.
Hackathorn has been working with a large high-tech manufacturer that recently completed a data warehousing project designed to combine information from its entire distribution network for BI and analytics purposes. As a result, the company can now track a product's origins no matter how long ago it came off the assembly line, Hackathorn said.
“One [thing] they said to me that really stuck is that the real opportunities for
improvements are in the cracks – the cracks between functional units,” he noted. “It‟s the cross-functional things that really trip you up.”
For example, a manufacturing process may consist of more than 200 individual steps involving different departments within a company as well as external suppliers. “One
department may be really doing their job well and taking their responsibilities seriously, and another department is doing likewise,” Hackathorn said. “But it‟s the handoffs between the departments where things can go awry.”
That in turn can lead to data problems that may wreak havoc with analytics results. According to Hackathorn and other analysts, one way to overcome cross-functional data issues is to create a unified data management and data governance program with detailed rules related to the handling of data by different departments. The data governance policies should be designed to help ensure that gaps don‟t arise in data collection, management and use.
practices, Davenport said. In-database analytics allows users to run data analysis
applications within a database or data warehouse, also potentially yielding reduced costs and faster development as well as the ability to embed predictive models in business processes and applications more easily.
But don‟t expect the data-related problems created by business analytics software deployments to disappear anytime soon.
“There are always issues around data quality, data governance and data integration across large organizations,” Davenport said. “That never goes away.”
Data visualization, social media analytics could be
keys to pervasive BI
By Jeff Kelly, News Editor
Business intelligence (BI) vendors and industry analysts have been talking about pervasive BI – "BI for the masses" – for years now. But, by most accounts, BI and analytics
technologies have yet to break through to the desktops or BlackBerrys of marketing managers, salespeople, shop floor directors and other business users.
A recent survey of BI end users and managers by the U.K.-based Business Application Research Center revealed that only 11% of respondents have BI deployed to more than 50% of employees in their companies.
Many factors contribute to the lack of business user adoption, but an important one is the technology itself. BI vendors are constantly touting innovations that will bring BI to the masses, but so far to no effect.
There is hope, however. Here are three technologies that could play critical roles in spreading BI to more business users:
Data visualization. Perhaps the most sure-fire way to spur business user adoption of BI is to improve data visualization technology. The easier it is for non-analysts to view and make sense of dashboards and other data visualizations, the more likely they are to use BI
technology.
A handful of vendors, both large enterprise software companies and smaller data
visualization specialists, have come up with enhancements to existing data visualization techniques to do just that. Among them is the ability to easily overlay multiple data sets on a bar graph or chart via drag-and-drop tools.
The amount of data that data visualization tools can analyze is also on the increase, thanks in part to in-memory technology. In-memory analytics engines load data into random access memory rather than disk, increasing query speed and lessening the amount of data modeling needed with traditional BI platforms.
And one open source predictive analytics language is enabling the creation of new types of data visualizations that make previous visualizations “look kind of tacky” in comparison, according to Marick Sinay, a financial analyst with a large multinational bank, who uses the technology on a daily basis. Called R, the free software language was designed for statistical computing and graphics.
Social media analytics. Social media analytics is an emerging discipline, and so are the tools that enable it. Currently, most social media analytics technologies require significant expertise to use and are far from perfected.
But Forrester Research Inc. analyst James Kobielus thinks that social media analytics tools – as they become easier to use – will be integrated into traditional BI platforms. That makes it more likely that non-power users will get their hands on the technology and understand what the blogosphere is saying about their companies.
Facebook, the world‟s largest and most influential social networking site, is doing its part to bring BI to the masses. The site offers page owners a number of analytics tools to monitor and measure referral traffic, demographic data and click-through rates, according to Alex Himel, a Facebook software engineer.
“By understanding and analyzing trends within user growth and demographics, consumption of content, and creation of content, [Facebook] page owners and platform developers are better equipped to improve their business with Facebook,” Himel said.
Unstructured data analysis. A related technology that could make BI more appealing to business users is unstructured data analysis.
unstructured. Improving the ability to access and analyze that data would probably prompt more business users to adopt BI technology.
Most current BI platforms are not well suited to unstructured data analysis, according to Forrester‟s Boris Evelson. And text analytics tools have yet to reach a level of maturity that would be inviting for non-power users.
But a couple of vendors are experimenting with integrating enterprise search technology with more traditional BI platforms in hopes of solving both problems, Evelson said. If successful, the new tools could prove particularly useful for marketing analytics, such as parsing user comments and reviews from online forums.
