Companies have been collecting data since businesses were first formed hundreds of years ago. Dry-goods stores collected information about their customers’ preferences so they could stock the right merchandise, and manufacturers took note of product sales so they could produce items that consumers would buy.
"Businesses may vary in their business analytics goals, but every company needs a strong technical foundation to ensure func¬tionality, scalability, agility and longevity"
But it wasn’t until the connected era that the volume of data began its rapid escalation. New methods of aggregating data, combined with lower costs of automation and storage, prompted companies to embark on massive data collection efforts. Now, companies are turning from collecting to connecting – extracting insights from data that enable operational teams to develop strategies for improved company performance and increased customer satisfaction.
The upfront costs associated with creating an infrastructure to support analytics and intelligence can be challenging for a business, but estimating the total cost of ownership over an extended period is an unquestionably daunting task. Unfortunately, many companies start developing data analytics projects without the proper planning, knowledge, resources, and labor, and then find themselves over-budget, under-provisioned, and in need of help.
How can companies approach data analytics projects to ensure they deliver a tactical advantage? There are five primary considerations that have the greatest impact on the success of this type of project: analytical readiness, scalability, agility, optimization, and expertise.
Business owners and delivery teams are often at odds about the feasibility and cost of developing a business analytics platform. Both the business and delivery teams need to be in alignment regarding what the underlying data is expected to provide, and the resources and timing necessary to create the analytical platform.
To achieve analytical readiness, teams should focus on best practices when it comes to data preparation, ensuring that the skills are available to evaluate and understand the raw data. Departmental siloes should be eliminated, with all teams engaged early and frequently in the planning stage to achieve logistical and realistic goals.
The number of mobile-connected devices now exceeds the world population, generating more data volume with potential value for a business. As a result, companies are faced with the need for additional infrastructure to handle the large datasets. Revisiting the architecture, planning and design is a costly endeavor, so scalability must be a factor at the outset.
When evaluating infrastructure options, it’s tempting to attempt to fully understand the volume of data to be handled, as well as the need for real-time results. However, the focus should be on defining an architecture that scales out rather than scales up. Also, consider the fact that different infrastructure environments have different programming requirements, and the type of user might suggest either a simple or complex interface. Some companies might find that intelligent data storage strategies create greater efficiencies and value, enabling them to compartmentalize data based on short and long-term needs.
Agility is not just about being able to handle more data points; it’s about preempting potential issues that can impact customers. This can only be achieved in a flexible environment where users can interact with the data and implement changes. This kind of ‘intelligently experimental’ approach is characteristic of a truly data-driven company.
When planning an analytics solution, consider leveraging advances in data visualization tools, allowing users to interact with data without having to write a line of code or even an SQL query. And plan for extensibility, with an ability to modularly add more analytics, create new associations and specify additional service mod-els in a timely fashion.
A company needs tremendous power to process, store, and read/ write the volume of data generated in the regular course of business. System requirements seem to ramp up daily as datasets continue to expand. Companies that do not continuously monitor a wide range of performance metrics can run into response delays, which can discourage widespread usage.
Virtualized solutions are one answer for providing dynamic scalability and elasticity. They also offer high availability for critical applications, and streamline the process associated with deploying and migrating new analytical applications. In addition, some out-of-the-box options offer comprehensive functionality in a single platform, providing quick results and ROI. The key to effective and sustained optimization is a strong architectural foundation; investing the time early on to implement best practices will yield substantial benefits down the line.
Companies need both industry-specific experts and data scientists with intimate understanding of the data being analyzed to draw true insights. Yet skilled data scientists are a hot commodity, and managing the subject matter expertise requirements means a heavy investment in training. In fact, the 2016 Gartner CIO Agenda reveals that while business intelligence and analytics are the No. 1 priority for CIOs for the third year running, 40 percent of CIOS also believe there is a large talent gap in analytics.
It’s important for a company to attract domain-specific data scientists. There is no substitute for industry-specific knowledge when it comes to building your knowledge base. An experienced data scientist with extensive knowledge and expertise on the subject matter is vital to yielding true insight. At the same time, companies should develop a creative, diverse team, and provide the smartest, most creative people from each department with access to data on a regular basis to ensure that analytics empower more intelligent decision-making.
Regardless of the cost, nearly any enterprise stands to gain value from sophisticated data analytics. Businesses may vary in their business analytics goals, but every company needs a strong technical foundation to ensure functionality, scalability, agility and longevity. Proper planning and strategy is a critical first step in developing a successful business analytics solution that delivers a measurable return on investment in operational performance and customer satisfaction.