Information Management: Strategies for Gaining a Competitive Advantage with Data (2014)

Chapter 15. Modern Business Intelligence-Collaboration, Mobile, and Self-Service

Organizing the Discussion and Tethering the User to Information

Organized and memorialized communication over information is also information. This chapter will teach effective collaboration strategies, no matter the data. Mobile usage is exploding and so should mobile access to corporate information. Learn why mobile access is better, why the time is now, and effective strategies for developing mobile applications for data. Users clamor to get IT “out of the way,” but both users and IT (or whoever the gatekeepers to data are) need to support the other’s needs. This chapter reviews those needs and where the balance can be struck.

Keywords

business intelligence; collaborative business intelligence; mobile business intelligence; self-service business intelligence

The data stores we are talking about in this book find their true value in an enterprise when the data is put to use. For most of the data access, that means a human¹ fetches the data for a unique use or utilizes a predefined data access such as a report. The old saw about business intelligence is that it gets “the right information to the right people at the right time.” It’s really time to add “in the right medium” to that mix. While this business intelligence ability is an entire discipline unto itself beyond information management, I felt it important to provide some guidance here, since it is the discipline that will utilize the information we manage.

Business intelligence is the sizzle to information management’s steak. It is the final step in data’s life cycle, and it must be done well. However, keep in mind that getting the data act together is 80% of the business intelligence battle. You simply cannot do business intelligence well without a robust information management infrastructure.²

FIGURE 15.1 Business Intelligence without a strong data foundation is easily toppled.

There are information stores that are exceptions to the rule of primarily serving user data access. Stream processing (Chapter 8) is a fully automated way of storing the data. Master data management does have a data access component, but primarily serves its data to other systems. Some of the NoSQL stores support operations directly as well.

There are numerous ways to fetch the data. The most basic and limited, yet still the most used, is reporting.

The accepted paradigm of effective business intelligence must change. Once it was exclusively reports built by IT from overnight-batch-loaded data warehouses, which replicated a single or small set of source systems. Those reports were deployed to the personal computers of “users” in what seems like, in hindsight, a very heavy-handed and resource-intensive process.

Just getting a report to a user’s personal computer on a regular basis is nowhere near the pinnacle of business intelligence achievement. Shops with this mentality are leaving tremendous value on the table.

Now, the considered norm for business intelligence involves “zero footprint” web-based delivery of reports. This improvement allows information to reach many more users. However, increasingly it has been noted that while the detailed, transactional data is absolutely necessary to be accessible on a drill-through basis, it is the rapid availability of summary level information that activates the process.

A majority of users have become more accustomed to a targeted presentation layer. Dashboards represent an advanced form of information delivery, second only to operational business intelligence as a mature approach to disseminating information. Building dashboards that already have a certain amount of knowledge worker intelligence built-in moves the organization towards real-time competitiveness.

I’m not going to delve into great reporting practices here because enterprises have largely evolved to the best practices for their shop. Detethering enterprises from basic reporting (query to database to screen) and into these more evolved forms of data access is going to be necessary for information management success. Today, this means considering the mobile user and incorporating collaborative features into the data access function and enabling a climate of “self-serve” unabated access for the user to the data.

The Mobile Revolution

The fix is in. Most, corporate workers and IT professionals understand that mobile-based applications will surpass traditional computing platforms in terms of users, application deployments, and enterprise importance. This will happen in the enterprise in the near future.

Think of mobility as the next huge wave for technology, following on the internet wave of the 2000s, which was preceded by personal computing, mini computing, and mainframe computing waves dating back to the 1960s. The mobile movement will extend to billions of people worldwide. It will advance in good times and as it will during regressive environments, during which time mobility will begin to replace landlines. It will have far more impact than the tethered, desktop internet.

With mobility, information is available without limits. Every location is like an office in some sense. The graying of the line between work and non-work continues in earnest. To some, this can cause grief, but it is a definite trend and represents a mindset for success in the workplace in the next decade—taking care of business when you can.

Mobile business intelligence is both portable, like paper-based reports, and interactive (and green) like internet-based activity.

Mobile devices continue to advance tremendously in terms of sheer computing power, capacity, speed, capabilities, and innovative techniques unique to the platform. With widespread public, enterprise, and private availability, Wi-Fi continues to blanket the planet, creating inexpensive ways to stay connected. The latest “generation” of networks goes mainstream very quickly.

“Apps” have become the new method of delivering. With highly focused functionality, apps deliver self-selected interesting functions that, in the aggregate, have vast adoption. As a means of accomplishing tasks quickly, apps have no peer. What works in the real world works in enterprises, and mobile devices are the embodiment of what enterprise information users want. The impact of mobility on business intelligence expectations means more than quick accomplishment of tasks. It also means solutions must deliver for:

• Low Latency—users will not tolerate slow response times

• An increased User Population—more people wanting to do more things with BI

Like water, information and analytics must flow through the path of least resistance, utilizing the deployment option that turns the information into valuable business action most quickly.

We have different expectations of and cognitive and emotional attachments to different devices. For example, we expect to do full, unabated authoring on the personal computer, little authoring on the cell phone, and somewhere in between for the tablet and other hybrids. Drill-through is the expected paradigm of data access on mobile. Mobile does make an excellent notification platform as long as it allows drill-through to detail.

Its “always on” nature alleviates the problem of real-time information delivered to an abandoned desktop, or to the web—with or without notice—that inherently involves multiple steps for the user to derive value and does not capitalize on available, real-time information. The answer is mobile business intelligence, or utilizing the mobile device as the data access layer.

Mobile Business Intelligence

Today’s enterprise has dispersed workers, virtual offices, and global presences.

When it comes to implementing mobile business intelligence, it will be more than a shrunken web- or desktop-based screen. Screen renderings will need to be delivered that make the best use of the space in a drill-down mentality. It should all provide functionality for all of the multitouch gestures that Apple and their ilk have gotten us used to through trial and error. These gestures include:

• Tap

• Swype

• Flick

• Double Tap

• Rotate

• Pinch

• Touch & Drag

• Two Finger Swype

• Touch & Hold

This makes a compelling argument for buying a mobile-enabled business intelligence platform as it will have the gestures built in. You will simply need to design—or review the recommendations for—each gesture.³

Other benefits and differences for mobile business intelligence is integration with the mapping programs on the mobile device for location awareness, integration with other apps on the device, and ability to utilize the information capture for QR codes, accelerometer data, bar code readers, and speech recognition.

Today’s enterprise has dispersed workers, virtual offices, and global presences. Today’s enterprise needs agility in information access and needs to be able to make decisions as soon as possible, and it needs empowered and collaborative workers. Mobile business intelligence is becoming an important part of the BI environment everywhere.

There are definitely challenges in rolling out mobile device support in an enterprise, let alone for mobile business intelligence. Those challenges include standards, the battery life, and signal loss. But, to be sure, mobile phones and tablet computers are business devices. They provide real-time business mobility. They allow users to abandon their desktops, which is what they need to do anyway, and there is little to no information loss in going to mobile.

Each organization must address its support infrastructure and mobile security needs in terms of which devices they will support and how to encourage the right security policies around encryption and cash credentials. This may necessitate the mandating of policies for remote wiping and password authorization of devices.

You can mobilize your existing reports. You can mobilize your dashboards. BI on mobile devices may be easier to integrate into your work processes than you think. Many business intelligence applications are being built exclusively for a mobile user today.

Self-Service Business Intelligence

By definition, it’s almost not analysis if an abstracted third party like IT personnel are heavily involved in data access.

On the opposite end of the spectrum, the business intelligence setup can almost anticipate the analyst session with the data. Rudimentary actions that would be taken based on a shallow view of data have already been thought of and taken in these environments. Those actions have been built into the environment, along with the standard business rules needed by the analyst. What’s left is highly substantial interactive analysis. If supported by modeled and clean data and a user-friendly interface, the analysis can go quite deep.

While leaving the appropriate analysis to the analyst, but removing the data collection and navigation hurdles, these sessions are much more productive.

Not only must the analysis go deep in order to keep a company competitive, each company must become more efficient in time spent by both IT and the (non-IT) business areas. The model of giving users the choice of (1) shallow analysis or (2) heavy IT/BI team involvement in analysis is not going to last much longer. Self-service BI techniques are necessary.

Self-service business intelligence can be viewed positively or negatively. To some, it’s a positive term connoting the removal of barriers to a goal. However, to some, “self-service” is a negative term, euphemistically meaning “no service” or “you’re on your own.” Self-service BI is about removing barriers and reliance on parties and processes that would stand between a user and his or her data.

If you put up a data store that is simply a copy of other data, only lightly remodeled from source, it usually carries many of the same data quality flaws of the source. It solves a big problem—making the data available—but after this copy of data, the fun begins with each new query being a new adventure into data sources, tools, models, etc. What has inevitably happened in some environments is that users take what they need, like it’s raw data, and do the further processing required for the business department or function.

This post-storage processing is frequently very valuable to the rest of the organization—if the organization could only get access to it. However, data that is generated and calculated post-data warehouse has little hope of reaching any kind of shared state. This data store is not ready for self-service BI.

To achieve these goals, you need a solid foundation and solid processes. Take account of your BI environment. While IT and consultancy practices have coined “self-service business intelligence” to put some discipline to the idea of user empowerment, some of it is mere re-labeling of “no service” BI and does not attain and maintain a healthy relationship with the user community or healthy exploitation of the data produced in the systems. We all know that IT budgets are under pressure, but this is not the time to cut vital services of support that maintain multimillion dollar investments. We will explore this deeper in the chapter on Organizational Change Management.

Self-service business intelligence is good for the bottom line, too.

In business intelligence, data integration is the most time-consuming part of the build process. This is undeniably true. However, if one were to look at the long-term, most long-term costs clearly fall into the data access layer. This is where the reports, dashboards, alerts, etc. are built.

Self-service BI is a mentality, not a set of tools, but there are tools that support this notion of self-service better than others. These tools attack a very important component of the long-term cost of BI—the cost of IT having to continue to do everything post-production.

There are a few areas that signify BI tools that work best with a user self-service mindset:

1. They perform fast—this allows a user, in the few minutes of time he or she has, to do an analysis to get to a deeper level of root cause analysis

2. They are seen as more intuitive—this empowers the end user so they can do more, versus getting IT involved, which stalls a thought stream and introduces delay that can obliterate the relevancy

3. They deploy fast—you can get to basic analysis very quickly and work your way into deeper analysis as needed over time

4. They offer collaboration, explained below

Self-Service BI and Outsourced BI

To truly set up business intelligence to work in a self-service capacity, you would overweigh the idea of working closely with users in the build process, which is a lever that gets deemphasized in outsourced BI. You would see business intelligence as less a technical exercise and more as an empowerment exercise. You would keep the build closer to home, where the support would be. And you would not gear up an offshore group to handle a laborious process of maintaining the data layer over the years in the way users desire. You would invest in users—culture, education, information use—instead of outsourced groups. And this is just what many are doing now.

Collaborative Business Intelligence

“Self-service business intelligence” is a good catchphrase for where BI needs to go. Tangibly, in addition to modeled, clean data and a “user friendly” interface, self-service BI (or at least most of the tools claiming the category) offer collaboration.

If you have recently done BI business requirements, measured BI ROI, or tracked BI usage through to the business action, you can see the value of collaboration. Other than collaboration, self-service BI tools exploit memory adeptly so they offer better performance and the UI is seen as more intuitive. I tend to think it’s the performance and collaboration that is most useful to users. Tool vendors may need to look beyond the user interface for their next release big splashes. Simpler UIs are in demand. Consider the Facebook interface. It’s simple, but used by 750 million users. Content, performance, and collaboration are king.

Elements of collaboration include an embedded discussion forum or a workflow component, making it easy and delay-free for the user community to cooperate and partner together to reach business conclusions. It includes star ratings, simple comments, interactivity, interfaces to internal social networks, unstructured data, security, and bookmarks. Sound familiar? Sounds like a modern web session to me.

Collaborative BI is the combination of Collaborative Interaction, Information Enhancement, and Collaborative Decision Making. It is collecting and memorializing the communication over data that would naturally occur otherwise. It facilitates that communication and accelerates it. Communication that would normally require phone calls and emails,⁴ can now happen within the confines of business intelligence. Communication enhances the value of the information assets we bring to the enterprise.

Among the elements of good collaborative BI are:

• Commenting

• Annotation

• Discussion

• Report Approval Workflow

• Prompts to action

Naturally, not all solutions that claim to be collaborative have all of these features.

Whether the scope of the collaboration is a data element or one of its uses, as in a report, is significant and requires design. For example, if a report is sales by region by month for this year, when does a comment about a low-performing region show up—for the report and/or the period and/or the region? Collaboration must be designed in this way.

Another good practice is to encourage face-to-face or virtual meetings among users where there is a high concentration of collaboration activity. This indicates an ongoing, and unresolved, conversation, and a meeting could be best for attaining closure.

Collaborative BI mirrors social media. Another artifact of good collaborative BI is voting buttons whereby decisions over data can be voted upon. Also, users can “follow” reports or their collaboration in much the same way people follow blogs and/or the blog comments. Users can follow “subjects,” machines, customers, regions, etc.

Users are also represented in a familiar way—with their picture!

Collaborative business intelligence, combined with a great data foundation, makes the very most of the limited analysis window of the user. Collaborative features should be considered essential in any business intelligence rollout accessing our information management stores.

Thinking Still Required

Despite my admonitions to automate business intelligence where possible and to bring the data access to a fuller level of readiness to serve the user through exception-based dashboards and intelligent use of smaller screen sizes, nothing replaces the value that the end user brings to the analysis of the data.

The question is not whether or not business intelligence systems will be able to truly think. Of course, they will not. Nor is the question whether or not BI systems will be able to create the illusory effect of thinking. Skillful business intelligence architects already create systems that pack enough “wow” effect to achieve a temporary transcendence for them. They will also clearly get better at it. Business intelligence displays apparently intelligent behavior when it automatically alters in-process promotions to be rerouted to prospect profiles that are responding to the initial mailing. When business intelligence reroutes procedures to best-of-breed providers, it is displaying intelligence. Additionally, when it changes pricing automatically in response to demand, it is displaying intelligence and providing extreme value to the enterprise.

Action Plan

• Assess the benefit of mobility to current business intelligence

• Assess the mobile strategy of your business intelligence vendors

• Consider the mobile option for all new business intelligence requirements

• Determine if collaboration is happening over business intelligence and the benefits that collaboration within the tools could bring

• Assess the collaboration capabilities of your business intelligence tools

• Give users more roles and responsibilities in business intelligence, up to the level they can handle, and then some

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