An increasingly common theme among many vendors exhibiting solutions at conferences is data science platforms. Some are more impressive and innovative than others but in this article, we’ll seek to address an important question on the minds of many: “Should I build, buy or wait before adopting Big Data Analytics?”
1. The Developmental Cost of Infrastructure
3. Scarcity in Manpower
4. Affordable Data Analytics — Myth or Possibility?
Not everything that can be counted counts, and not everything that counts can be counted. — Albert Einstein
Big Data Analytics isn’t exactly new. Large corporations have already been storing and analysing multi-data types not only to gain competitive advantages but also to achieve deeper insights into customer behaviour patterns that directly impacts their business.
Building a Data Analytics solution is somewhat like building your own house. Just like everyone who wants to build their dream home, every business wants to build a perfect data analytics solution. However, the single, most common denominator for both cases is that one doesn’t truly have an idea on what the true and final costs might be until the end.
The cost of building a customised Data Analytics solution can be broken down to 3 main components:
- Human Resource
Building any major big data solutions like an analytics platform requires significant design, coding, testing and documentation work. The infrastructure consists of data storage, servers, network and monitoring tools. All these costs are proportional to the platform’s size. It can involve much more actors than those listed above due to the inner complexities that lies within big data. We should also remember that as data evolves, infrastructure costs will too.
In the early stages of implementation, working out the kinks and teething problems can be a time-consuming and laborious process. After a system is implemented, it is routine to anticipate ongoing application improvements, the addition of hardware and networking firmware, new software versions, patches, bug fixes and new features – all of which further complicates the overall maintenance process.
Amongst all this, the most significant cost is human resource. The solution is complex, because it requires real know-how and involves numerous specialists. The most important hires would be Big Data engineers (who are in very short supply), in addition to Infrastructure experts who assist with systems maintenance, Java/Python developers, database administrators (DBAs), data analysts, dashboard developers and so forth.
Regardless of whether your business chooses to build an in-house solution or buy an existing “off-the-shelf” solution, launching new analytical capabilities into your business is akin to launching a new product. You must take into consideration: computational power, storage, networking architecture, software integration, data space design, analytic framework design, data visualization, and types of analytics required to support your overall business objectives.
The entire process can take more than a year to implement and that’s already being optimistic. Before rushing into building any big data solutions, every organization should carefully consider the costs, benefits and time needed for implementation. Quicker implementation means organizations can achieve significant insights and recoup costs more rapidly.
Looking to adopt data analytics and don’t know where to start? You can start a conversation with us today. DataVLT is an outsourced analytics solution that comes with a PaaS platform that helps enterprises make meaningful sense of their data. Find out how we can guide your business through its transformation journey here.
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