Big Data and Analytics are complementary to each other. As big data is increasing the need for data analytics is also increasing day by day.
The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.
It has been observed those companies which have injected data analytics into their business can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition. But somehow it is not easy as it not only required huge investment but also commitment from the management. Decision makers scratch their heads while formulating strategies for big data analytics.
McKinsey has stated three planning challenges which has to be faced by corporate while making strategies for big data and analytics.
1.Alignment in Investment and Business strategy
In a company data come from various sources like transactions, operations, and customer interactions. Integrating all of this information can provide powerful insights, but the cost of a new data architecture and of developing the many possible models and tools can be immense an one has to make choice in that time. For example planners at one low-cost, high-volume retailer opted for models using store-sales data to predict inventory and labor costs to keep prices low. By contrast, a high-end, high-service retailer selected models requiring bigger investments and aggregated customer data to expand loyalty programs, nudge customers to higher-margin products, and tailor services to them.
biFor continuity of the smooth business it is advisable to have an alignment between business strategy and Investment. One should find out at what time what kind of investment has to be addressed first and which one has to be take care of later and accordingly the plans should be made.
2. Balancing speed, cost, and acceptance
A natural impulse for executives who “own” a company’s data and analytics strategy is to shift rapidly into action mode. Once some investment priorities are established, it’s not hard to find software and analytics vendors who have developed applications and algorithmic models to address them. These packages (covering pricing, inventory management, labor scheduling, and more) can be cost-effective and easier and faster to install than internally built, tailored models.
3. Ensuring a focus on frontline engagement and capabilities
Even after making a considerable investment in a new pricing tool, one airline found that the productivity of its revenue-management analysts was still below expectations. The problem? The tool was too complex to be useful. Problems like these arise when companies neglect a third element of big-data planning: engaging the organization. When describing the basic elements of a big-data plan, the process starts with the creation of analytic models that frontline managers can understand. The models should be linked to easy-to-use decision-support tools-call them killer tools-and to processes that let managers apply their own experience and judgment to the outputs of models.
But planning for the creation of such worker-friendly tools is just the beginning. It’s also important to focus on the new organizational skills needed for effective implementation. Far too many companies believe that 95 percent of their data and analytics investments should be in data and modeling. But unless they develop the skills and training of frontline managers, many of whom don’t have strong analytics backgrounds, those investments won’t deliver. A good rule of thumb for planning purposes is a 50-50 ratio of data and modeling to training.
Part of that investment may go toward installing “bimodal” managers who both understand the business well and have a sufficient knowledge of how to use data and tools to make better, more analytics-infused decisions. Where this skill set exists, managers will of course want to draw on it. Companies may also have to create incentives that pull key business players with analytic strengths into data-leadership roles and then encourage the cross-pollination of ideas among departments. One parcel-freight company found pockets of analytical talent trapped in siloed units and united these employees in a centralized hub that contracts out its services across the organization.
When a plan is in place, execution becomes easier: integrating data, initiating pilot projects, and creating new tools and training efforts occur in the context of a clear vision for driving business value-a vision that’s unlikely to run into funding problems or organizational opposition. Over time, of course, the initial plan will get adjusted. Indeed, one key benefit of big data and analytics is that you can learn things about your business that you simply could not see before.