One of the positive aspects of implementing BI is the ability to constantly improve upon access to analytics, end user interaction, and overall use. Due to increasing storage capabilities, enhancements in features and functionality, and flexibility in deployment methods, organizations have more to consider when looking at expanding their current solutions. In addition, when looking at BI analytics specifically, businesses are tasked with taking into account what types of analytics they want to apply and what data is the best to use as a starting point.
In general, many considerations exist when applying analytics within an organization. Whether a first time implementation or a mature BI environment, analytics applications should take into account certain external factors. For the purposes of this article, five aspects are discussed to provide a starting point for companies looking to take their business intelligence to the next level. These considerations should serve only as a starting point, since the following points do not encompass all things that a company must consider before expanding upon their business intelligence analytics environment.
1. Build vs. Buy BI Analytics
The debate over building a solution versus buying an off-the-shelf BI tool and customizing it is common within many organizations. Depending upon current BI use and the IT infrastructure behind the scenes, the value of one over the other may seem obvious. In some cases, IT developers prefer to design their own solutions due to the unique business needs of the company or the industry they represent. Although solutions can be customized, the amount of time required to implement or to expand upon an existing solution may equal the effort involved with in-house development.
For organizations with a mature BI infrastructure, the ability to add to what is currently being used may be an obvious extension of BI use. Many enterprise organizations are looking to increase the value they get from their BI infrastructure. Part of this involves transitioning from traditional BI to operational and predictive analytics. The expansion of analytics and its value includes looking at BI in a new light – for instance, identifying the effects of a new store on overall sales and product distribution, or looking at historical customer trends, comparing it to market performance, and building out predictive models.
2. End User Expertise
Considering the audience is always important when designing, selecting, or delivering a solution. The technical expertise and role of the individual determines the type of optimal interaction. Super users may be able to access raw data directly to create their own set of analytics, whereas most business users will need additional guidance. Whether this comes in the guise of wizard driven dashboards, parameter based reports, or pre-developed interactivity, the complexity of data doesn’t change. However, the way in which it is accessed does change. This means that the development or delivery of analytics requires interaction with business users and a general knowledge of the audience and their comfort level with direct access to analytics.
3. Business Rule Development
Business considerations represent part of the requirements gathering within an analytics project. Linking business and IT requirements means identifying the business rules needed to develop the statistical analyses and algorithms necessary to bring the analytics to life. In general, business rules refer to the interactions of data leading to the output of analytics. For instance, just in time delivery of products within the supply chain means that identifying when products need replenishing is essential. Automated processes are required in these instances, as well as alerts. Sales and marketing can apply the same types of rules to identify discrepancies or to pull out inconsistencies by developing constraints to discover discrepancies in performance.
4. Data Preparation
General data preparation is the activity that causes the greatest misunderstandings between business units and the IT department. Business users want immediate value out of their BI access, and IT understands that value comes from preparing data and developing strong data integration procedures and practices. Additionally, organizations should consider data quality and other data management initiatives to ensure continued valid data over time.
Even if an organization currently delivers analytics, the addition of new applications means that the data needs to be reviewed. It is not enough to take what exists and add to it since the business rules may necessitate a new starting point. Either way, for each expansion to an analytics initiative, the data behind the user access point is what provides the overall value. Therefore it makes sense to spend time to identify the right data and set aside a realistic amount of time to take into account general preparation and data integration activities.
5. Looking to the Future
Whether developing an in-house solution, identifying how a BI solution will be used, or implementing data management processes, the reality is that long-term analytics usage needs to be taken into account. Although not always easy, companies should look at their current use, short-term analytics projects, and the next implementation phases. In some cases this involves expanding usage across various departments, and in other cases expansion means increasing the complexity of analytics used and general applications.
BI Analytics and Next Steps
The expansion of analytics usage within an organization is a natural evolution process, and an organizations’ BI use is rarely static. Between advancements in technology and outgrowing current applications, businesses can expect to increase the value of BI and analytics over time as they mature in their overall use. Planning for this eventual expansion involves several considerations including those discussed above. To ensure successful expansion of BI analytics the importance of end user interaction, business rules and data preparation, development, and future considerations cannot be overlooked.
About the Author
Lyndsay Wise is an industry analyst for business intelligence. For over seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay is the channel expert for BI for the Mid-Market at B-eye-Network and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. She can be reached at firstname.lastname@example.org. And please visit Lyndsay's blog at myblog.wiseanalytics.com.
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