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Avoid These Pitfalls When Choosing a Business Intelligence Solution

Business intelligence software and services have gone mainstream

Business intelligence software and services have gone mainstream. In the past, only large enterprises typically invested in BI solutions. These days, however, more and more small and medium-sized businesses are actively looking for ways to tap into their information and use it to increase revenues, reduce expenses and realize competitive advantage.

Even though the concept of business intelligence has been around for over 20 years, a substantial percentage of BI implementations are still considered failures. Given the large number of vendors selling BI products and solutions, one would think that at least one of them would be able to get it right. Why are successful BI solutions so elusive?



The answer lies in how success is measured. The only way to establish the success of a BI project is to determine the return on investment (ROI) realized by the company as a result of deploying the system. The problem is that BI deployments usually require very large up-front investments in software, hardware, analyst services and developer time. After the system goes live, there are also high ongoing costs of customization and maintenance. By the time the company realizes whether or not the effort is actually improving its business metrics (months or years later), so much time and money has been spent that discovering a positive ROI is difficult. A key factor in achieving positive ROI is fast, hands-on business user success – when they are able to actually use and benefit from the software within a short period of time.

The best way to avoid this common trap – and to make sure that you quickly and indisputably demonstrate the value of the business intelligence solution you choose – is to select an option with very low up-front costs and fast implementation times.

- Don't Pay ANYTHING before your Users are Actually Using the Solution on Real Data!

This piece of advice may sound unrealistic – until you become familiar with BI solutions based on modern technology. With traditional BI solutions, which use software based on older technologies, it is inevitable that you will have commit substantial time and money to your BI project long before your users ever get their hands on it. This is a risky, outdated approach and one best avoided.

The value of a BI system can only be known once the business users are actually using it to answer important questions and solve real-life business problems. In almost all cases, this requires a process of revisions and improvements during which users want to incrementally change how the system works based on how they are using it. If they cannot quickly add new questions and analytic processes to the software, it may prove only marginally valuable. At worst, they simply won’t use it and the entire investment will be for naught.

Unfortunately, most BI vendors require the customer to invest in serious hardware and data integration/preparation work before the end users even get to play with the data and attempt to adapt it to their own business processes. In typical systems, most changes will require modifications to the back-end data structures and/or report definitions – adding customization costs, taking time and frustrating your users.

The goal has to be to get your users gaining real-world benefits from a BI solution with minimal up-front investments of time and money. Using today's BI technologies, you can deliver an initial working solution using the standard PC hardware you already have, with no more than a day or two of data preparation (sometimes much less). A standard desktop PC with 12GB of RAM can handle huge amounts of data (even 500 million rows) with reasonable response times. That should be enough to get started. Get a more powerful server machine only once you understand exactly what your users will be doing. (Maybe you won't need one at all.) For your users, there are several user-friendly, drag-and-drop reporting/analytic tools. This should get them started quite nicely, and most of those have free trial versions!

- Beware of Data Warehouses and OLAP Cubes

A data warehouse is a centralized repository of data, usually organized in specific ways designed to make it efficient for an OLAP cube, which in turn speeds up query response times to previously-defined queries. If this sounds too technical, that’s because it IS too technical. And like anything very technical, it requires technical people and several weeks or months of work to set up. Thereafter, maintenance and improvements are also time-consuming, constrained and expensive.

Modern BI technologies (e.g., column-based storage and in-memory query processing) do not require a data warehouse/OLAP architecture in order to provide excellent query response times, simply by better utilizing hardware resources. Going down the OLAP path, especially before the system has gone live, is a sure way of punching a big hole in your IT department's budget long before you even know if the system is worth anything to the actual business users it is intended to serve! Data warehouse and OLAP implementations are complex, risky, time-consuming affairs and difficult to maintain. These days, there are better alternatives! Try them first. The implementation time is significantly shorter and easier, so your up-front investment will be much smaller.


- Consider the Scalability of the BI Solution you Choose

Successful BI systems almost always face three ongoing pressures for expansion: (1) the quantity of data with which they must function is ever-growing, (2) the demands from users for new and ever-more-complex queries/reports/analytics keep coming and (3) growing numbers of users in the organization will want access to the system.

These demands often prove extremely challenging to those who maintain a BI system. Scalability issues are usually addressed by either throwing more hardware at the problem or making architectural changes to the system. Both approaches result in system down-time, IT management overhead and significant expense. OLAP cube-based solutions are particularly vulnerable to scalability challenges.

When you are evaluating BI solutions, consider at what rate you expect your data to grow, how user demands might tax the system down the road and how many users might potentially need access to the system in the future. Ask BI vendors what kind of hardware would be required to handle your future expected demands and what limitations the system might introduce. For example, you will never want to be forced, for reasons of system architecture limitations, to divide your data into separate silos or to significantly limit the amount of historical data available. These are both common vendor-suggested approaches which introduce extensive data management headaches and business-level reporting constraints. If you are going to compromise on this, you better know you're not doing it because the solution is using out-dated technology.

Consider carefully the costs and limitations you may face down the line. You don't want to invest in a BI solution with a foreseeable expiration date.

- Outsourcing vs. In-house BI

If your business does not have people with the in-house skills and knowledge required to implement a BI solution, outsourcing is clearly the way to go. Even in this case, it is important to be very involved every step of the way, making sure that any choices made by your consultants are in line with your own strategic and tactical priorities. The downside of using outside people to implement and maintain your BI system is that your access to the system is very limited. When you need new reports, for example, your requests may get delayed because your service provider is simply too busy doing other things.

It is always a good idea for an organization to manage its own BI, whenever possible. Since BI is a core strategic asset and source of competitive advantage, updates and improvements to a BI system need to be flexible and swift. With traditional BI solutions, your lack of in-house expertise may entirely obviate this option. However, BI technologies have evolved tremendously over the past few years, and you might be surprised at how the expertise you do have is more than enough to deploy and maintain a powerful BI solution. In most cases, a sophisticated business user with solid Excel skills will be able to integrate large amounts of data from multiple sources and create the interactive dashboards and reports your business needs with no need for consultants or IT professionals.

- Think Twice before you Run your Data in the Cloud

Despite the hype, cloud infrastructure is expensive. For light processing of modest quantities of data, it's great. However, BI solutions are resource-intensive computing applications, requiring extensive amounts of memory and CPU horsepower. Using a cloud-based BI solution operationally will siphon off a lot of your budget to your cloud provider or force you to significantly limit the amount of data available for business reporting.

If you have compelling reasons to run your BI in the cloud, it is very important that you choose a BI solution with cloud-friendly technology. Look for a system optimized for efficient use of hardware resources under cloud conditions, e.g., to utilize more hard disk space (cheaper) and less RAM and CPU (expensive). Otherwise, you will be limiting yourself to small data sets or expending huge budgets. Either way, that may be fine in the short-term, but down the road you might not be so happy with your BI choices.

Read the original blog entry...

More Stories By Elad Israeli

Elad Israeli is co-founder of business intelligence software company, SiSense. SiSense has developed Prism, a next-generation business intelligence platform based on its own, unique ElastiCube BI technology. Elad is responsible for driving the vision and strategy of SiSense’s unique BI products. Before co-founding SiSense, Elad served as a Product Manager at global IT services firm Ness Technologies (NASDAQ: NSTC). Previously, Elad was a Product Manager at Anysoft and, before that, he co-founded and led technology development at BiSense, a BI technology company.

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