Business Intelligence and Business Analytics

In a world that’s awash in data, assembling the right information about customers and business conditions has never been more difficult. It’s a paradox that Jay Dittmann, vice president of marketing strategy at Hallmark Cards in Kansas City, Mo., knows all too well. Having 13 million loyalty program members who purchase thousands of different greeting cards and other items makes tracking trends a daunting proposition. “There are mountains of data to sift through and limits to our marketing budget,” he explains.

About two years ago, Hallmark deployed a business intelligence (BI) and business analytics (BA) initiative to better understand buying patterns at more than 3,000 Hallmark Gold Crown stores across the United States. The company wanted to better nurture its relationship with its frequent buyers and, through predictive modeling, determine how to market to various consumer segments during holidays and special occasions. As a result of this initiative, Hallmark has boosted sales, while also simplifying and improving the analytics process.

Business intelligence and business analytics aren’t new concepts. The idea of understanding the relationships between bits and bytes of data extends back to the late 1950s, and BI has been around in earnest since the late 1980s. However, today, the ability to aggregate, store, mine and analyze data can make or break an enterprise. As a result, BI and BA have emerged as core tools guiding decisions and strategies for areas as diverse as marketing, credit, research and development, customer care and inventory management.

In fact, BI and BA are evolving rapidly and meshing to meet business challenges and create new opportunities. Although nearly all Global 5000 organizations already use these tools, 35 percent of them fail to make insightful decisions about significant changes in business and market conditions, according to IT consulting firm Gartner. What’s more, the task isn’t getting any easier as data streams become more intertwined, and mashups and other Web 2.0 environments pull data from multiple sources.

The bottom line? “Business intelligence and business analytics are on the cusp of a major change,” observes Joseph Bugajski, senior analyst at Burton Group. “There is a shift toward providing deeper insight into business information. And there is a growing emphasis on better tools and putting more powerful and better software in the hands of business decision makers.”

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5 Strategies for Implementing Data Analytics in Hospitals

Since the advent of the Internet, people have been able to access a significant amount of information with the click of a computer mouse. As technology becomes more ingrained in healthcare delivery, providers are facing the quandary of how to glean useful information from the enormous amount of data available through electronic health records, computerized provider order entry and other databases. Steve Nitenson, RN, BSN, MS, MBA, PhD, adjunct professor at Golden Gate University, San Francisco, and senior healthcare solutions architect for Perficient Healthcare, shares five strategies hospitals can use to implement healthcare data analytics. 

Goals and challenges

"Healthcare analytics is the Holy Grail with respect to healthcare," Dr. Nitenson says. The conversion of loosely net patient data — data that is loosely associated with other data — into meaningful, actionable trends holds the promise of increased coordination of patient care, patient safety, quality of care and cost-efficiency for not only individual patients, but also for patient populations at large. Gathering and using meaningful information on patient populations is one of the major goals of accountable care organizations. "Part of the accountable care organization model requires the ability to analyze healthcare data," Dr. Nitenson says. However, turning patient data into usable information is challenging; furthermore, once healthcare providers can access the information, determining how to act upon it poses more problems.

Dr. Nitenson differentiates between data — the facts entered into a database — and information — the interpretation of the facts in a meaningful context. "Just because you have a lot of healthcare data doesn't mean you can do anything with it. It becomes mission critical, especially today with all changes occurring in healthcare, to get as much data converted into information that is actionable," he says. According to Dr. Nitenson, the healthcare industry is currently able to access 80 percent of available healthcare data and can convert only 20 percent of that data into meaningful information.

Strategies 

1. Establish a governance structure. Hospitals should set up a governance structure to manage implementation of healthcare data analytics capabilities. The CEO, CMO, CNO and CIO should all be involved, Dr. Nitenson says. The CMO and CNO need to communicate the kind of information they want to the CIO, who has the IT knowledge to conduct the actual implementation. "You need to have a governance structure [in which] the CIO takes the lead but has the [CMO and CNO] to always ensure that whatever he or she is doing is going to meet their needs," he says.

Hospitals should also consider partnering with a professional organization experienced in data analytics, Dr. Nitenson says. In this situation, the IT organization would develop analytics and the CIO would implement it with the support of the CEO and guidance of CMO and CNO. "[Providers'] business is delivering healthcare, not developing healthcare analytical data models for themselves," Dr. Nitenson says. He says the learning curve would be much steeper and the time to implementation longer if the providers try to create an analytical toolset on their own. "It's a major undertaking that takes time and a good deal of effort. [If providers do it on their own] it usually ends up on the back burner and never gets done." 

2. Define the desired information. Due to the large amount of healthcare data available to hospitals, from everything from lab data and physician notes to insurance claims to medical records, hospital leaders need to define what information they want. "You have to be able to filter what's really important to you based on the hospital and specialty [you're interested in]," Dr. Nitenson says. Leaders should also determine when they want the data, how they want it presented and who they will share it with.    

3. Format the data appropriately. One of the keys to analyzing healthcare data is presenting it in an appropriate format. For example, Dr. Nitenson says if the hospital wants to understand the lab data for someone whose blood is drawn twice a week for five weeks, simply looking at the 10 data points would not yield any useful information. "It's meaningless if there's no reference point," Dr. Nitenson says. Instead, the hospital would need to trend the data and benchmark it against the demographics of the region the hospital is located as well as national averages. 

4. Secure the information. Once hospitals have converted the healthcare data into meaningful and actionable information, they need to decide who to grant access to and establish security protocols to ensure access is available only to those individuals with "a need to know," Dr. Nitenson says. He adds that at this point, sharing healthcare data becomes an ethical question. "The more information you get, the higher order of ethical behavior one has to have." For example, he says exposing the information to insurance companies could have consequences for hospital reimbursement. Access to information does not have to be all or nothing, however. While patients should be able to obtain their own information, hospitals should consider what information might be inappropriate. "Some information may be deleterious to patients without understanding the context," he says. 

5. Share information effectively. Even if healthcare data has been converted to information and the information has been secured, data analytics cannot produce benefits of improved quality and reduced costs if the information is not shared effectively. Dr. Nitenson suggests using a "push" rather than a "pull" technique for sharing information with physicians and nurses. "Push the information to the professionals," he says. "Present it to them in their daily work. Don't [make] them try to find it." The difference between "push" and "pull" is similar to opt-out and opt-in systems. In a "push" environment, physicians would be automatically presented with information that they would have to consciously ignore or dismiss — or opt out of. In contrast, a "pull" environment would require physicians to find the information themselves, or opt in. The former method increases the likelihood that the healthcare professional will be aware of the information they can use to improve patient care.

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Do you need Business Intelligence?

It’s a classic question that has a classic answer – companies need to translate data into information in order to make strategic business decisions.

Companies continuously create data whether they store it in flat files, spreadsheets or databases.  This data is extremely valuable to your company.  It’s more than just a record of what was sold yesterday, last week or last month.  It should be used to look at sales trends in order to plan marketing campaigns or to decide what resources to allocate to specific sales teams.  It should be used to analyse market trends to ensure that your products are viable in today’s marketplace.  It should be used to plan for future expansion of your business.  It should be used to analyse customer behaviour.  The bottom line is that your data should be used to maximize revenue and increase profit.

All companies produce reports from the data they collect from their business activities.  Every manager has a manager who needs reports unless you’re the CEO in which case you just need reports.

Some important questions you need to ask are:

1. How much resources (i.e. people, time, dollars) does it take to produce these reports?

2. Am I sure that the data in these reports is accurate?

3. Am I concerned with the security of these reports?

4. Am I receiving these reports in a timely manner?

If the answers to these questions are too much, no, yes, and no then you need a Business Intelligence solution.

IT are the first people to begin the process of creating a report.  They need to extract the required data and pass it to the person creating the report.  That person then has to spend time manipulating the data to create the required report.  This process can take many hours, even days, of effort.  And this process needs to be carried out for each and every report that the company requires.

Business Intelligence solutions automate the process of extracting data and producing reports thereby eliminating all of the manual effort of IT and the people creating the reports from raw data.

A number of studies have been conducted on spreadsheet errors.  An often cited report, What We Know About Spreadsheet Errors by Raymond R.Panko of the University of Hawaii, concludes that “every study that has attempted to measure errors [in spreadsheets] has found them and has found them in abundance”.  If decisions are made based on inaccurate reports then these decisions are more than likely the wrong decisions.  This could lead to disastrous results for the company involved.

A Business Intelligence solution produces reports using data that has been automatically extracted from a cleansed data source (typically a database or data mart) to produce accurate reports.  In order to make important business decisions, for example, as to what new products to carry or what products to drop, it is vital that managers have accurate data in the reports on which they base these decisions.

Data security is a very real problem.  As soon as data is extracted to spreadsheets the potential for abuse is greatly increased.  Spreadsheets can be “lost”, private corporate and sensitive data can be copied onto a number of portable devices, and laptops can be stolen or misplaced.  Cases where private data is made public through negligence occur daily. Think Wiki.

Business Intelligence solutions take advantage of existing security infrastructures to keep private data secure and within the company.  Data within reports is typically presented to employees via the company’s intranet and employees are given access to only the data they require to carry out their specific job functions.

Without a Business Intelligence solution companies may have to resort to dumping vast amounts of data into spreadsheets from their databases.  This in itself is a manual and, in most cases, an extremely time- consuming task.  The spreadsheets then have to be delivered to the person creating the report.  Spreadsheets then have to be consolidated and the data manipulated manually to produce the desired reports.  All this takes time and the data within the reports may be days or weeks old by the time the reports are complete and delivered to the manager.

A Business Intelligence solution provides real-time reports directly to the manager on-demand from any location.  The data in these reports is typically as recent as the data in the data-source it is being extracted from which allows the manager to monitor the business in real-time.  The manager can then base decisions on what is happening now and not last week or even yesterday.

There is a reason that Business Intelligence continues to show up on CIOs’ priority list.  The amount of data being stored by companies is growing exponentially and it needs to be managed.  It needs to be secured and distributed efficiently to enable employees to make important up-to-date business decisions. CIO’s are beginning to understand the realities of this problem and are working to implement Business Intelligence solutions that fit their particular company’s requirements.

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Comparing self-service BI and traditional BI

When used successfully, self-service BI tools address one of the biggest frustrations users experience with traditional tools: the lag time between a request for a report or dashboard — or even a simple change to an existing report — and the response to that request from IT or the BI team.

“It’s not just that companies are short-handed on the BI IT side, but it’s also the fact that users increasingly want to do more things with data,” said James Kobielus, senior analyst at Forrester Research Inc. in Cambridge, Mass. Self-service BI is the “hottest segment of the BI market” among his client base, he said. It “gives users their own tools to pull data from source applications directly, or from an intermediary database that IT sets us for users.”

Data Analytics for Hospitals with Ideal Analytics

When IT is taken out of the report and dashboard development process, the BI process is more agile, meaning companies can put their insights into action faster, Kobielus said.

Self-service BI not only puts the power in the hands of the user, but also opens up new ways of analyzing data, Kobielus said. Many users have been exposed to visualization, reporting and modeling to a degree in Excel spreadsheets, but they haven’t been given easy access to predictive analytics tools and statistical modeling. “These self-service BI tools are embedding predictive modeling and statistical modeling in self-serve, highly user-friendly drag-and-drop user interfaces,” he said.

Of course, IT still does a lot of things behind the scenes, as Spott’s approach demonstrates — integrating data, developing templates and showing employees how to use self-service BI tools’ drag-and-drop wizard-driven interfaces.

For Spott, the power of self-service BI is not in its GUI — which he believes is pretty much the same across the available tools — but in how well it handles metadata to connect to multiple data sources. In his view, that is the biggest challenge for IT groups when they choose a data tool. “What I was looking for was a tool that could make dynamic connections on the back end to multiple data sources across multiple platforms,” he said. “That’s where the challenge usually comes in: being able to join different data sources simultaneously.”

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