The importance of big data analytics in business

Traditionally, IT costing efforts have been done, if at all, at a higher or macro level. For example, total capital costs for data center construction along with associated annual operating costs for things like power, floor space, cooling, etc., or budgeting for server or storage resources on a yearly basis are based on forecasted business growth scenarios.

In today's distributed systems world, any type of cost allocation has been, in most cases, coarse at best. Sometimes IT costs will be equally shared by all organizations using the total infrastructure but this approach leads to, at best, political tension, and at worst drives organizational behaviors towards acquiring access to resources outside the influence and control of IT policies and procedures.

Most financial organizations have some type of asset database that includes information on all data center resources, when they were purchased, the price, some type of amortization schedule, and some level of annual operating expenses associated with these assets.

Typically this information is owned and controlled by the financial side of the organization. Additionally, there is typically some source of information that relates these assets to business units, services, and/or applications that they are used to support.

Most IT Operations organizations have multiple tools (in most cases too many!) that monitor and measure the availability and performance of all IT technology resources. Furthermore they have one or more sets of tools and approaches by which they are measuring their ability to successfully deliver service to their various lines of business as well as customers.

Most data center management teams have a fairly complete understanding of their data center floor: power capacity, equipment footprint layout, total cooling capacity, and costing information such as cost per square foot.

To date these three disciplines within organizations have traditionally never operated in coordination with anything other than anecdotal, ad hoc, or manual communications. But there is a huge opportunity for value added through close collaboration, the goals of which should include:

  • Finance places a currency value on the business work that IT resources are actually accomplishing.
  • Data center management understands how much work the data center is or could support over time.
  • IT operations cost-effectively ensures the delivery of acceptable service within their ever declining budget constraints.

Each of these three main domains has a very large, multibillion-dollar solution market ecosystem built around optimizing use cases within each domain individually. For example there are hundreds of server, storage, and network management and monitoring solutions for performance and availability management of IT resources.

There are many dozens of DCIM solutions for data center management of the physical data center. And there are a plethora of solutions for financial and asset management. All of these solutions were designed around use cases that were solely within their domains and the needs of the associated end-users and therefore capable of accepting only metrics and data sources from within those domains.

Until very recently software solutions have not existed that would allow or facilitate a more seamless and productive collaboration across these organizations in support of achieving these goals. However, recent technologies in data access and analytics are lending themselves to productively attacking this challenge of intelligent and proactive collaboration across these disciplines and tool sets

The Importance of Good Analytics in Business

How can IT leaders take existing business information and make better informed and more rapid decisions that will allow them to really cost and performance optimize their entire infrastructure? Because at the end of the day, the use cases of IT are always going to be different than the use cases of business in the context of analytics.

An example business use cases of analytics would be for things like, "we want to rollout a new marketing campaign in a new geography and we want to understand what a reasonable expectation of sales penetration will be based on past campaign behaviors and our similar demographics. We can correlate it with past sales activities and demographics and we can forecast that we will have a change in demand for our products or services by X amount."