October 10, 2015
Today technology enables to get data from different views and do high end analysis of data. Business analytics tools can build top down reports, detailed reports and slick looking charts and dashboards. But if all this analysis is not aligned with organization performance goals or is not able to bring improvement in core processes, it is not of much use. Business analytics should enhance the value of information available and aide in achieving organization goals.
The business analytics strategy must allow the organization to reach its goals. It should enable transformation and innovation. Here is a 4 point plan to create a business analytics strategy that will allow the company to transform itself such that it reaches its planned business goals and improve upon them over a period of time.
1) Understand Real Requirements for potential transformation objectives
The organization must nail down the vision for the transformation and the key transformation objectives. The team should them elicit the real requirements that are to be built in order to achieve the transformation. For example, banks earlier had reports on customer data and his transactions. A bank can have the objective of customer stickiness. The bank will retain the customer in his entire financial lifecycle and maximise sale of financial products.
2) Get the Right Data and right architecture and manage them effectively
In order to transform business using business analytics, the basic component is to get the right data and manage it properly. The data derived has to be correct, consistent and updated. Everyone working on the business transformation should have the same data set. This will help in effective collaboration among different departments and facilitate good decision making. Tools for data storage, data analysis and retrieval have to be such that using data is efficient and they support the objectives of transformation.
Data architecture should be such that analytics capabilities can be leveraged well. You might need to extend infrastructure to improve reporting, analysis and forecasting capabilities. The right IT environment will help in discovering data relationships and draw new insights that can help the business. The architecture should ensure that the data is secure and private. Good data governance should be an underlying objective all the time.
For example, if a customer contacts the bank for opening an account but never really comes back to open the account, his data can be used and sent to the sales team who can contact the customer to persuade him to open the account. If a customer has balance overdue on credit card for some time or regularly, the bank can make an irresistible offer for a loan. Analytics can get such data to banks easily and if departments collaborate and manage data effectively, multi-channel customer relationship is possible.
3) Execute steps for transformation
Once the right resources for the analytics strategy are in place, it is time to execute steps for business transformation. It has to be started with the smaller use cases, measure success and then go ahead. If quantitative and qualitative analysis show success, then the team can go ahead with the transformation plan else the plan will have to be modified. The sales department of the bank will always produce reports that show targeted customers, customers gained and the ratio between the two. It will also produce reports to show cost to attract customers versus revenue earned from customers. But the new analytics solution will collate a report from various points such as the reception desk of the branch, Internet queries regarding interested potential customers and send it to the sales team who can then devise and execute a strategy to convert these queries to customers. The sales team can get data on customers who have defaulted on their credit card payment and approach them with loan products. Customers who have large amounts in their bank account can be approached by wealth managers to help them manage their money better. This will be another revenue stream for the bank.
4) Use analytics to measure if the transformation objectives are valid and add value to the business
You have to measure the effectiveness of the investments in analytics and evaluate to what extent the transformation objectives are being met. If the transformation is for increased sales, check sales reports. If it for improved processes, check metrics that show process improvement like shorter time taken per transaction or cost reduction in each transaction. Are the analytical reports helping managers make better decisions, get new opportunities etc.? If it can be proved that there are improvements using business analytics,
stakeholders will be more receptive to it and you will get funding and approvals easily else people will not adopt it.
Data is the most important raw material for a business analytics strategy. Data has to be recent, accurate and consistent. Data has to be secure. If data is mishandled or compromised, analytics will not give proper results.
There are many reporting and analytical tools available to manage huge amount of different type of datasets fast and correctly. The organization has to take a strategic decision on the software and hardware to use. It depends on legacy systems, data requirements etc. Hadoop is an open source framework that uses hardware and open source software to manage and analyse data.
I cannot stress enough on leadership. It is the most important component. People working need direction and guidance and the leadership plays a big role in adopting business analytics for transformation. It should have the vision of what is to be done in this area.
People are of course critical to developing a business analytics strategy. The organization has to consider existing talent, gap in skills and how to reinforce the team with necessary skills. There should be a dedicated team with the responsibility for the business analytics strategy. when developing or expanding an analytics capability within a healthcare organization.
The Strategy implementation plan should plan for achieving goals of the analytics strategy. The plan should vet the stakeholders and their objectives and decide what has to be included in the project scope.
1) The team should be put in place. It has to be a mix IT specialists and business team members. There should be proper management and prioritization of routine tasks and transformation related tasks so that conflicts are avoided.
2) Structured and unstructured data sources must be identified and methods for data identification, data cleansing, storage, transfer and use should be planned and implemented.
3) Software and hardware that is required to implement the analytics strategy should be selected and sourced. Typically a data warehouse, middleware tools, ETL tools and OLAP tools are required. Integration tools will also be needed. Existing infrastructure has to be studied and then necessary software and hardware should be procured.
4) The transformation has to be done in a phased approach to minimize risks. Smaller use cases have to be implemented and once the success is evaluated, the team should move to bigger use cases.
5) The plan should devise metrics to measure the effectiveness of the analytics strategy in achieving the transformation objectives. The metrics should be qualitative and quantitative. In the bank example, metrics could be something like number of loan products sold to credit card customers or number of bank account related online queries converted to bank customers. The plan should be evaluated at various stages of implementation to compare the benefits vis-a-vis costs.
Here are examples of real successful implementations of business analytics used to transform business –
A major American department store chain wanted to push promotions aggressively. It took them 2 months to plan and implement promotions which were too long. It realised that it already had a lot of data from the customer and product perspective. It also had data on promotion material. To transform its business for speedy promotion and tailor offerings for customers across different cities and states, the store used analytics. The organization collected data on customer profile, customer preferences and competitors’ promotion in different areas and stored it in a cluster from a brand/product perspective. The clusters were individually studied and personalised promotions became precise and also took lesser amount of time – about two weeks only.
A major online financial news site with over 2 million subscribers started collating customer data through registration. Using the registration details and the consumer’s consumption of content, trend in consumption and devices used to read, the company built digital consumption signatures for different news segments – business, entertainment, personal etc. The data collected helped them analyse and create different customer personas. The content was then created, published and distributed as per the persona. This transformed the news site’s business and processes –
– They could understand customer preferences better and personalise content
– The transformation helped in the news website getting more subscriptions than the newspaper.
– The analytics strategy helped them develop intelligence and have targeted advertising campaigns.
A comprehensive analytics strategy if planned and implemented properly helps to bring about a transformation in the business for the better.