Everybody has heard of analytics, but very few know its implications
Analytics is the present buzzword, and enterprise analytics solutions are being leveraged by most of the enterprises today. However, implementing set notions about analytics and actually using analytics to derive meaningful intelligence that can transform into solid returns in terms of revenue and productivity are two different things.
The latter requires an in-depth understanding of how enterprise analytics can influence every department across every business area. If this is imbibed in the enterprise work culture by the top management, and implemented by the employees, the enterprise can reap significant benefits making enterprise analytics their most trusted secret weapon to beat competition, and emerge as an invincible market player.
How Enterprise Analytics differs from other analytics
The term Enterprise analytics denotes something more than just analytics. On top of traditional analytics, it enables the enterprise to visualize the data and form conclusions based on the observed big data trends. In essence, it:
- Predicts the customer segments that will show interest in future products/services.
- Forecasts sales numbers, so that production quantities can be determined.
- Shows which products/services if given on discount/bundled together can raise the sales revenues.
How to wield Enterprise Analytics correctly to derive optimum outputs
- Identify the right data channels, and use them in business scenarios.
Enterprises have several channels that generate real-time data that is logged into the analytics monitoring solution constantly. With such huge amounts of data (which counts as big data), important trigger events can be easily missed. Enterprises also make the mistake of deploying complex EDW (Enterprise Data Warehouse) solutions where only small scale analytics can suffice to provide actionable intelligence. Hence, it is very important to study the target market, and the customers and interact with them to shortlist only those channels that need to be considered in the analytics gathering exercise.
- Data access should be made easy for authorized employees, so that they can innovate.
The enterprise analytics solutions should provide access to authorized employees across departments after due authentication. Easy and fast access makes the analytics solution to make a few trade-offs in data accuracy. Hence, due vigilance should be maintained by the data logging teams and the data accessing teams in identifying and cross-checking data that goes against the uniform data trends of a particular channel.
- The right analytics tools and structures are the key difference between a successful enterprise and a failed one.
Both minimalist and all-inclusive approaches to using analytics tools are damaging to the overall functioning of the enterprise. The reason being, the major tools used should be consistent across departments to enable collaboration. A single tool might not fit the requirements of all departments and multiple tools might make the system too complex to train the employees for each tool. A mixture of BI and analytics tools is generally recommended for an enterprise.
- Reproducible processes help save crucial time-to-action in critical situations.
Analytics models that identify data patterns and erroneous spikes should be implemented, so that the data monitoring systems can respond to events like system failure, request overload, etc. in the absence of a human intervention. This level of automation makes the analytics solution capable of handling minor incidences on its own. Also, in case of strategizing after considering data visualization indicators, the analytics solution should come up with suggestions that can be taken up by the brainstorming team.
- Metrics are the true measure of the success of using enterprise analytics (a combination of BI and analytics).
After implementing business strategies based on previous observations of data trends, the analytics should change positively. For example, increased sales, increased customer loyalty, decrease in employee attrition rate, increase in the number of new customers, etc. The top management should share the effect of using enterprise analytics based strategies to the employees to encourage similar decision-making suggestions from them. If these positive changes are not seen, the strategies should be modified accordingly within short times. Basically, metrics will determine the level of experimentation required in the enterprise’s business strategies.