The hype around Big Data is just that, BIG. Even though Big Data is predicted to be the future fodder for all analytics. The all-pervasive, all-knowing Big Data is indeed useful, and in the crosshairs as the spending target for most marketing teams in the next fiscal year.
The hype around Big Data is just that,
BIG. Even though Big Data is predicted to be the future fodder for all analytics. The all-pervasive, all-knowing Big Data is indeed useful, and in the crosshairs as the spending target for most marketing teams in the next fiscal year. The budget allocations for it will be proportional to the size of the enterprise. However, is pumping marketing dollars into it really useful? The researches show otherwise.
A major gap in the promise and usability of BD is the effort required to process the information it provides, and deriving useful knowledge from it. Researches show that the usage of these techniques by marketers in practice has gone down by almost 19% despite the increase in the funds allocated to them. There are several reasons for this gap.
1. Information, not insights – Most analytics tools process data to provide information. However, the need of the hour is to dig deeper and uncover richer insights like customer behaviour, patterns etc.
2. Too much time – The time taken to run analytics and get results is too long to be useful sometimes. If the results get to you post your marketing planning cycle, they are worthless.
3. Not sure what to do with the data – So you get the beautiful reports. Now what? Potential users of marketing analytics may not have a strategic planning process or marketing decision making process that builds in a step to use available analytics.
4. Too generic – Sometimes the reports are too generic, and not customized to the enterprises’ specific needs.
5. Creator-user divide - Producers and users of marketing analytics sometimes do not have a strong relationship that prevents the analyst from understanding or anticipating users’ needs.
6. Lack of training-Users does not have sufficient training to understand marketing analytics. This includes simple analytics tool training and a crash course in regression. Unless the users understand the data, there is no chance that he / she will be able to make decisions based on it.
7. Data itself is flawed – The data collected itself sometimes is qualitatively and quantitatively deficient. Companies fail to define the datasets what will help them collect the deep insights that allow them to enable the marketers to take strategic decision.
8. Focus on existing, not doing more – The primary focus of the marketing teams is to strengthen their position in their existing business, not on exploring new avenues of growth. The wholistic growth of the enterprise can be step changed by exploring greener and newer pastures. This is something most teams are yet to figure out.
9. Accurate, but not inspiring – Analytics have to be accurate. However, there are areas where the results might not inspire due confidence. For e.g. mining through customer opinions in textual formats is a painstakingly slow process, but one that is very important if you want to know what is being said about you.
10. Management oversight – The focus on the trending and analysis has to come from the top. The managers have to be involved in not only defininig the teams, but also in defining the metrics, data sets, inputs, types of analysis, and the meaning of the results etc. over the course of the project.
The benefits of utilizing big data are great, but the challenges are also significant. While efforts are on to tackle them, unless the focus shifts from the size of Big Data to its impact, it is just chasing pipe dreams.
