Top Business Intelligence Technologies

In a data-driven environment, Credit unions need to keep up with the rapidly transforming financial industry. Business Managers are now drawn to the use of Business Intelligence (BI) technologies for insights about customer behavior, identification of areas of improvement to further streamline operations and engagements, improvement of overall efficiency, etc. Modern BI technologies put the power of data in the hands of the business users from IT-led reporting.

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According to Gartner, a leading industry analyst firm, BI is an umbrella term that includes the application, infrastructure, technologies and best practices that enable access to and analysis of information to improve and optimize decisions and performance,

Unlike other data fields like machine learning, big data, etc. BI focuses on interpreting data to provide business leaders with the visualization and insight that data can provide, which positions them to make better decisions. BI is not same as big data which primarily focuses on the generation and management of huge amounts of varying data from a variety of sources. Machine learning involves developing statistical models from datasets to help organizations do a better job of forecasting, while BI is a system that has the ability to handle data generation, collection, analysis, reporting, and forecasting.

Credit Union data leaders face countless choices when selecting the right BI tool that will deliver value to them and give them an edge over competitors.

Key considerations when acquiring right business intelligence technology

Software acquisition is a critical concern to firms worldwide, and the magnitude of its impact on the inner workings of firms is expected to grow. BI is no exception. Currently there are many BI products ranging in power from simple reporting technologies to sophisticated BI platforms. Business owners and chief information officers must consider various factors in their decision making.

Here are the key considerations to keep in mind when acquiring new BI Technology.

Integration and Compatibility with existing technologies

The more compatible a BI tool is, the higher the chance it will be adopted by the organization. Most banks already have operational systems like ERP or CRM in place, so they will want a BI tool to be an ‘add-on’ to their existing IT set-up rather than a stand-alone system. Most financial institutions already have existing database technologies as well as enterprise portals. Sixty percent of organizations are analyzing and integrated data from at least six data sources according to a Ventana research study. Adding BI technologies to the existing systems will cut down implementation costs, improve product adoption among end-users, increase productivity and efficiency and permit users to blend data from various sources while creating relationships with them.

Functionality

One of the key criteria every chief information officer must consider is the functionality of the BI tool they wants to employ. Since there are so many BI technologies in the market, these technologies offer different functions and it is important for the information officer to understand the business key performance indicators (KPIs) and the right tool to analyze them. Data discovery is an important function when considering purchasing a BI tool. This feature allows the users to get better understanding of the data from different sources within their organization. Another key feature one must consider is the associative user experience. Most Traditional BI solutions use predefined paths for data exploration while others allow users to take whatever route they want (they work the way the user’s mind works). The BI tool must have a variety of visualization tools like bar, line, pie, area and radar charts as well as pivot tables, heat maps and the ability to combine two or more of the tools .

Education and Ramp-up

The sole purpose of a business intelligence tool is to put the power of data in the hands of business users. Business users should be able to ramp-up on the technology quickly otherwise it becomes tedious to maintain. In a study conducted by Information Week to find the most important factor for realizing the value of a software deployment, 70% of the respondents chose “how effectively the user community adopted the software.” There is a strong correlation between user-friendliness and user adoption. The shorter the learning curve is for training a new user, the better it is. Training the users on how to use the BI tool is a challenge because there is no single way to approach it. The BI tool must provide collaborative materials like audio-visuals and ‘how-to’ tutorials on the user’s profile which can help bring the user up to speed which increases the adoption rate.

Maintenance

The Maintenance of a BI platform in a financial institution plays a major role. A study shows that 90% of software life cost is related to its maintenance phase. The BI vendor must be committed to constantly improve the software since the needs of a business change swiftly. Business owners and CIOs must note that either an IT employee or an outside third party will have to review the KPIs on the scoreboard/dashboard, modify security settings, or perform software updates. These tasks can result in a large maintenance cost down the road.

Price

Cost is obviously an important factor for any business expenditure. It is wise to ensure that the selection of a BI tool is based on fit of purpose measures (the number of users, the number of metrics tracked, the duration/number of the license). In a survey of 20 enterprises in the SME sector that have implemented BI systems, 18 said the most important factor to consider when implementing a BI system is the price of the system and its implementation. The BI tool price covers internal implementation labor (deployment costs), data warehouse hardware and software, training costs and ongoing support labor (maintenance costs). As an organization evolves with their operation processes, costs resulting from upgrade policies, consultation tool customization must be considered.

Support and Services

Often times when a BI system has been implemented, glitches will pop up or an end-user can’t find out how to work a particular feature on the system. The CIO must address which kind of support service will be available after the implementation of the system. Some solution companies may require you to hire dedicated support staff of your own or train an in-house staff. The options mentioned above come with their own dedicated costs.

Deployment

The mode of deployment affects the selection of BI system because of the implementation time and cost. For traditional client server architecture, the client software must be developed and installed on every client node whereas for web based architecture, the web browser will be used to connect with the server. This approach will save cost and time.

Market Reputation

Start by reading through reviews online or get offline referrals who may have used the platforms that you are looking for. Check if the offering is from a reputable vendor.

Determine as well if the BI technologies you’re considering are listed in the CULytics Solution Gallery and have reviews from peer credit union leaders. Knowing that the vendor has good reviews ensures that it has a high level of commitment for the credit union industry. Moreover, a reputable vendor helps build trust and eases the worry on whether the company is investing on the appropriate BI technology.

Best BI Technologies for Credit Unions:

1. BIRST

BIRST has evolved as one of the premier BI analytics tool in this sector. Built on top of a next-generation multi-tenant cloud architecture and driven by machine learning technology patents, this tool allows organizations to get consistent data at a relatively fast pace.

Considerations

A key phase of data analysis is the extract, transform and loading phase. Where most BI technologies require a solution to perform this process for them, with BIRST, raw data is imported into this platform, it automatically carries out ETL to process the data making it ready for deep analysis and reporting. BIRST offers an automated ETL-process which saves the organization the cost of purchasing an independent ETL solution.

BIRST’s platform is cloud-deployed system. For a system to be sole cloud based it must be relatively flexible and scalable compared to other BI technologies. Another advantage this option offers is that the total cost of deployment and implementation is relatively cheap since there is no need for on-site equipment and installation.

BIRST offers a wide range of connectivity with CRM and ERP systems as well as other BI technologies like Salesforce and Tableau. This feature allows developers to build their own connections with other third-party vendors to explore more possibilities.

Although BIRST offers good user interface for end-users, they may be required to go through some training to be brought up to speed to fully implement the software features. Another reason why the learning process is difficult is the limited online resources/ materials. Compared with other BI technologies that have vibrant online user communities, BIRST has a lot of work to do in growing their help community.

Although BIRST offers a better deployment cost thanks to its web-based approach, the pricing plan may still turn off prospects who are interested in purchasing their tool because their price plan is not transparent.

2. Domo

Rated as a robust and effective self-service tool, Domo finds itself among other top BI technologies as a result of its wide connectivity and cloud based system. Most users prefer Domo because of its approach focusing on top management executives experience with its user friendly interface to help make quality decisions.

Considerations

Domo is positioned at the top of the BI niche because it is a mobile-compatible platform with cloud delivery that gives top executives freedom to access reports and dashboard via IOS and Android apps on the go. All the functionality on the desktop is available on the mobile.

With the Domo App store, users can create and customize their apps and blend data from different sources (third-party integrations) to perform a robust data analytics.

Aside from its friendly user interface that enables users to develop a story via its data storytelling toolkits, Domo Buzz allows employees to communicate in real time as well as trigger instant notifications as significant data changes occur.

Although most companies are excited about the cloud deployment Domo offers, some companies still prefer on-site deployment especially when they have existing on-site solutions. This option might be less business friendly for financial institutions that need an ‘add-on’ solution.

3. Microsoft Power BI

Launched in the 2013, Power BI is Microsoft’s data visualization solution. This is a cloud based BI and analytical service that has a range of tools that can meet an organization’s needs. This self-service BI tool was planned as an add-in for the Microsoft ecosystem but evolved as a functional tool used by banks to systematically scrutinize their data and share insights. Power BI offers fully customizable visualizations options from Bullet Chart, Calendar Visualization, Table, Heat Map, etc.

Key Considerations

With the Power BI desktop app as well as 1GB Data Storage Capability, one can import flat files on their system and create, view and share personal dashboards and reports for free. For a subscription fee of $9.99/user/month, an organization can acquire the tools to create, publish and view organizational content packs while consuming live data sources with full interactivity and other features.

A Microsoft Excel User can comfortably use Power BI with little or no support thanks to the data analysis expressions (DAX) scripting pattern.The DAX scripting style is a relatively simple construct used to calculate columns and measures.

With a robust community of millions of users and access to learning materials via independent learning platforms like edx.org, new users can hit the ground running in little time without the aid of customer support.

Power BI has a connectivity pack that can seamlessly connect data from various solutions like Google Analytics, CRM, Salesforce and other open standard-based APIs. This allows data analysts to pull data from various sources into a tool ready for analysis.

Every data scientist know that data cleansing is part of the extract, transform and loading phase of data science and a BI tool must have the ability to clean the data especially when pulling from an external source. This feature is limited in Microsoft BI.

4. MicroStrategy

MicroStrategy has evolved through time and is still most data analyst’s choice for carrying out data analysis as well as data visualization.

Considerations

With the use of a mobile device, end-users can prepare data ‘on the fly’. MicroStrategy is available on Windows, Android, Mac, Linux and iPad, so it offers a wide range of access that allows users to engage with this platform and carry out analytics.

Leading B2B companies use MicroStrategy because of its five V’s effects: Volume (it can handle data that up to petabyte), Variety (it can analyze structured data and unstructured data), Velocity (the data processing time is relatively fast), Veracity (the processed data is trustworthy) and Value (business users can use the processed data to make quality decisions)

Most end-users of MicroStrategy have noted that there is a steep learning curve to the technology and there is a call to improve their KNOWLEDGE BASE Site.

5. SAS Visual Analytics

An integral part of SAS Enterprise Solutions, SAS Visual Analytics combines BI and analytics to discover and gather insights from enterprise data. The use of this tool has helped eliminate guesswork and provides a platform for senior executives to spot trends, identify patterns and project future outcomes with analytics.

Considerations

SAS has an built-in business analytics framework to help data analysts navigate and explore the workspace so designers and programmers can easily build reports and dashboard.

By adopting the multi-dimensional expressions (MDX) based functionality, SAS has unleashed the possibilities for developer to build highly customized reports that can carry out drill down analysis. This model eliminates one-dimensional reporting.

About 50 percent of computer users use Microsoft Office on their system, SAS offers strong synergies with MS Office.

6. Sisense

Sisense is one of the finest BI technologies in the industry because of its focus on investing in the technology at the foundation of its product. Sisense offers companies that have limited IT resources and big data to analyze terabytes of data by multiple users through a single commodity server thanks to its in–memory, in-chip and single-stack architecture.

Considerations

One feature of Sisense that excites its users is the drag-and-drop user interface and eye-grabbing visualization on an interactive web-based dashboard. This encourages those with little or no programming experience to carry out quality data analysis regardless of its volume.

Sisense has a unique capability to handle complex and disparate data thereby eliminating the need for multiple tools like data warehousing or OLAP cubes to analyze big data all on a single commodity server.

Since Sisense can run on a single commodity server independently, this causes the demand on the company’s IT department is less compared to other BI technologies. This feature encourages simplicity in the system as well as less maintenance costs.

Sisense’s dashboards are web-based. When accessed on real time, they are great but it doesn’t offer powerful scheduling or print features for reports. Most business users usually generate reports on their dashboard and carry out presentations outside their BI tools via other formats. One drawback is the lack of report sharing Sisense provides.

Sisense offers limited mobile functionality, it’s not as dynamic as the rest of the platform which makes for poor user experience on the go. This could turn off some prospects.

Most Credit unions employ BI technologies for reporting and dashboard creation as well as predictive analytics. This predictive analytics mode will help them to improve their business processes, enhance their decision making, etc. Sisense’s predictive analytics functionality needs more maturity before it can become the right tool for a financial forecasting task.

 

7. SpotFire

TIBCO Spotfire is a BI tool that focuses on providing users with executive dashboards, data analytics and data visualization in three different packages: desktop, cloud and platform. This tool is designed to meet the users needs whether it is in a small organization or a group of companies. SpotFire’s core capabilities are data access, wrangling, visualizations and maps which can help a user meet their business needs.

Considerations

Spotfire offers a recommendations wizard that suggests best-practice visualizations based on the data the user selects. This is a mild hand-support Spotfire provides to support inexperienced users. Other support TIBCO Spotfire offers are online tutorials, documentation as well as a vibrant online community.

SpotFire native’s connectivity to R projects enables it to deliver advanced statistical analytics, data augmentation and predictive/prescriptive analytics in real time. With this feature, the end user can drill down to find root causes of business issues.

One reason why most companies employ TIBCO Spotfire is its ability to share reports and other analytical resources quickly and effectively with other users in basic formats like PDF/MS Powerpoint. This process can automated.

Some users may find it difficult to customize visualization. This limits the user to fixed visual tools that might not capture the information they want to showcase.

8. Tableau

Tableau has become the gold standard in business intelligence and is most the data analyst’s choice tool. Tableau offers both desktop and online platforms which allows users to access the software anywhere (in the cloud and on site). Given that most credit unions have a lot of member PII (Personally Identifiable data) and sensitive data, Tableau cloud usage may not preferred by them.

Considerations

Tableau’s data visualization feature is high quality and it makes the tool stand-out among others. Its robust drill down and visualization tools makes data approachable for any data users. Tableau’s web browser edit capabilities gives business users light-weight editing capabilities for the dashboards within browsers without using desktop licenses.

In an era where users access data on the go, Tableau has mobile apps for IOS and Android giving users mobility and data on their mobile devices. This robust mobile responsive tool offers web authentication, making it easier to have a seamless sign-in experience even in a proxy set-up.

With its simple drag and drop features, this tool allows users to easily access and analyze data, build reports and dashboard and share key insights across the company.

In their latest release, Tableau has improved on their REST API so BI developers can integrate with external sources using the get request. With their connectors, you can interact with other data sources like SAP, big data and DB technologies to improve the data analytics quality.

When a rookie signs up on Tableau, he/she won’t feel alone thanks to the number of users in the community. The community has various forums and groups where users can interact with each other. Blogs and release notes are also available as well as the resource library that offers challenges and dashboard exhibition.

For a small business who wants to acquire a BI tool for their data analysis, Tableau might not be the most cost effective choice for them. For mid-size financial institutions, they will need to install Tableau Server for enterprise service.

For a credit union institution, every staff should be available to receive certain reports at a schedule defined by the administrator. This feature is not fully functional in tableau. For end-users to export or print certain reports, they must manually initiate the process and everything they need to extract data from the platform unlike other technologies which are automated.

Although Tableau has a wide range of data connectivity from Hadoop, SQL and even cloud sources, it can improve on its data integration capabilities when preparing the data for analysis. Tableau is great when there is a data mart or well-defined cubes but in the absence of these functions, it is a not tool for data modeling.

9. QlikView

Qlikview is another very popular BI technology. It is well known for its product quality as an integrated BI platform that comes bundled with adequate demos and training manuals. Qlikview is easily deployable and configurable, and starts producing results very quickly.

Considerations

QlikView focuses speed, and users can locate and gather information on the system faster than other BI platforms. Its search is likened to Google’s search tool.

Using an in-memory engine approach, Qlik enables users to discover and analyze data for patterns that most BI technologies can’t offer. With this tightly controlled analytics experience, organizations can explore their data and find insights that will position them to make better decisions. Data can be refreshed in real-time thanks to its in-memory processor.

Unlike other BI technologies, Qlik provides a report scheduling and distribution feature called NPrinting. This enables users to share reports in different formats (MS Office and PDF).

Designed to handle large amounts of data, Qlikview can do this at a relatively fast pace but this features depends sole of the server RAM. CIOs must take note of the server RAM size before considering loading larger sets of data.

Although Qlikview has lot of online resources as well as a vibrant user community, one has to have a good knowledge of SQL as well as basic programming skills since developing reports on Qlikview requires a bit of coding.

Conclusion

The enormous amount of data generated in the financial sector due to the daily transactions made by customers at the go, ever-changing Forex rates, interest rates etc. There is a need for credit union leaders to make timely decisions in a data driven world. The business intelligence solutions have the capacity to unearth the potentials these data have and help credit union leaders answer key questions that involves their operations, customer behavior and even their product cycle. As financial institutions become more dependent on information, collaboration and integration with multiple data sources (internal and external), the need to use the right BI tool to help management make the data driven decisions is imperative. Buying decision play a major in every organization hence data leaders and CEO of credit unions must possess the appropriate knowledge for the right tool as well as employ the tool that will serve their organization now and tomorrow.

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  • Decision Minds is a top Solutions and Services company in the analytics space.  I used them while at Neustar.  Superb work across use of On-prem and Cloud Analytics.

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should-ceos-attend-the-culytics-summit
the-cost-of-a-wrong-decision
biggest-roadblocks-in-becoming-data-driven
a-journey-for-all-organizational-maturity-levels
maximize-your-data-analytics-checkup
navigating-the-data-analytics-landscape
improving-data-literacy
why-credit-union-leaders-should-invest-in-their-teams
why-credit-unions-should-not-invest-in-building-predictive-models
why-should-measure-the-success-of-data-analytics-program
cost-of-choosing-the-wrong-data-analytics-technology-stack
why-data-analytics-strategy-focus-on-supply-and-demand-side
kpis-to-measure-the-success-of-data-analytics-program
data-analytics-for-credit-union-branch-heads
data-organizing-principles
top-data-warehouse-storage-technologies
discover-the-hidden-truth-behind-watermelon-kpis
unveiling-the-hidden-dangers-of-cobra-effect-on-kpis
are-you-accurately-interpreting-your-kpi
unmasking-biases-a-guide-to-data-analysis-and-kpi-definition
uncover-the-power-of-proxy-kpis
unraveling-the-hidden-impact-of-sampling-bias-in-credit-unions
bi-department-structure
hidden-impact-of-confirmation-bias-in-credit-unions
getting-executive-attention-for-your-data-analytics-program
uncovering-biases-in-data-preprocessing
navigating-missing-data-in-credit-unions
navigating-sampling-bias-in-cu
unleash-the-power-of-real-time-data-use-cases
how-confirmation-bias-impacts-cus
breaking-down-selection-bias-in-credit-unions
unmasking-reporting-bias
elevate-your-cu-with-data-analytics-expertise
understanding-and-tackling-volunteer-bias-in-credit-unions
time-period-bias-in-credit-union
overcoming-biases-in-credit-unions
embracing-the-future-fast-future-fundamentals-program-equips-cred
unlock-growth-and-efficiency-credit-unions-guide-to-generative-ai