If Gartner’s word is anything to go by, Big Data adoption is seeing an uptick. The analyst cites “increasing inquiries” into Big Data analytics tools, as more businesses look for new opportunities in capturing increasing amounts, or eek more value out of the large amounts of data they already own.
Supporting this, a US-based study into budgetary plans, indicated that 60% of CIOs believe Big Data will be a ‘top driver’ of IT spending.
Generally speaking, a collective shift in the industry is rarely a coincidence. Trends are usually propped up by a series of concrete benefits, and in the case of Big Data, this is no different.
Companies with a well actioned Big Data strategy can make more well-rounded and informed decisions. One of the key uses of Big Data is to get a better understanding of the market, prospects and customers.
Data is sometimes referred to as the “oil of the 21st century”, and customer data specifically, is arguably the key factor in that. Online and digital business models, and notably Social Media, has opened up a two way dialogue between people and the rest of the world, and provided businesses with an unprecedented level of meaningful data insight.
As a result, businesses now know more about their customers than ever, and this information can be used to earn new ones.
In gaining a better understanding of the market, Big Data can be used to gauge potential market interest. As well as indicating whether a new service or product is worth providing, this information can also help businesses forecast supply with greater accuracy, in relation to demand.
As well as understanding external factors, Big Data can also provide new insights to understand internal operations and process efficiencies. The data can can highlight capabilities and processes that are ripe for improvement, and be used to guide the best course of action to optimization.
Why you need EA and DM
When businesses get it right, Big Data can open a lot of new doors, and allow a business to reach new heights. But simply collecting the data isn’t enough. To return to an aforementioned analogy, much like oil, Big Data isn’t of much use in its raw form. It needs to be refined, and concentrated into something decipherable, and greater than the sum of its parts.
Both Data Modelling (DM) and Enterprise Architecture (EA) are essential in making the most out of this refinement process. Data Modelling helps you to analyze the data by providing a contextualized perspective of the information across various platforms. Enterprise Architecture helps you translate and apply data to strategic business and IT objectives. It also aids in indicating which data insights are a priority within your current-state organization and which data will be critical to support your future-state.
This is great news for businesses who already have established a functioning EA and/or DM initiative, but those behind in terms of architecture and modeling will have to find room in the budget for new tools.
In the past, this would have always been a daunting exercise. Encouraging stakeholder investment into EA especially has been notoriously difficult. High, local installation costs and long term contractual commitments are enough to make any business think twice, especially when the business is trying to stay agile. - and this goes doubly for a specialist profession such as EA, where business leaders and stakeholders might not be fully aware of the potential gains.
However, the introduction of Software as a Service based tools has provided the aforementioned apprehensive businesses a new life line. Local installation costs and long term commitments are avoided, in favor of flexibility.
What’s more, is that some Big Data Analytic tools officially support integration with Data Modellers, benefiting alignment of processes and systems.