In 2012, as a society, we were generating around 2.5 exabytes of data each day1. That is 2,684,354,560 gigabytes, or enough data to fill up 41,943,040 iPads (the 64GB ones). Companies are turning to analytics in order to put all this data to use in a meaningful way, so it doesn’t just pile up. Considering that much of this data is user generated, data analysis can provide actionable insights and a better understanding of why users behave the way they do. Insights like why users in a certain online game leave when they do, or why a consumer adds an item to their cart on an eCommerce platform without ever completing the purchase. Being able to understand this data has given a number of companies an edge over their competitors according to recent Bain & Company report.
While the benefits may seem quite obvious, this is a new and growing sector and so far less than 1% of all data is analyzed in any form2. The first question that needs to be answered once a company has decided to implement data analytics, is whether it should build a custom solution from the ground up, or buy an already existing one, or use a combination of the two. However, there are pros and cons to each of these options, making the decision all the more difficult.
By building a platform from the ground up, ideally, a company is able to customize it to the exact specifications that it requires. It can save money by leaving out features it doesn’t need, while including features that may not be included in a pre-built solution. However, a company needs to have the resources to build an analytics solution from scratch, and those resources are not easy to come by, and are usually expensive, and it needs to be willing to put in the time as well. It needs skilled developers who can construct it, maintain it and offer support and training to employees, which can be very expensive. In addition to being able to build the system, these developers would also need to have the expertise to integrate analytics into existing platforms in order to collect data for analysis. Since there are so many different platforms that are out there, from advertising networks, to gaming, mobile, finance, etc. these developers would need to have a very wide range of expertise, which may have very little to do with the company’s core competence.
On the flip side, buying a prebuilt solution can save money, as it will come with its own customer service and training departments, and will likely have a larger and more dedicated developers who just focus on analytics, making it easier to integrate with pre existing solutions and data sources. The biggest downside to such a platform is a potential lack of customizability. In many cases, companies can’t specify exactly what questions they need answered from their data, but rather are limited by what the prebuilt solution can offer. Additionally, since it comes pre-built, the company may end up paying for features that it doesn’t actually need.
Both building an analytics platform and buying one have pros and cons, so the best solution may be a combination of the two, a hybrid of sorts. Using a prebuilt platform that is easy to customize and integrating it with pre existing solutions and data streams will save money, while still providing the most comprehensive analytics solution. This would allow most companies to benefit from the lower costs and customer service of a prebuilt system, while still giving them the custom solution that they want and need.
The bottom line is that the total cost of ownership of building your own analytics tends to be far greater than its potential return. The question is therefore not build vs. buy, but which one of the new analytics platforms out there is the right one for you.
Download our dedicated white paper to see what’s right for you.