Most medium to large companies already employ a professional data analyst, and many smaller ones are also jumping on the bandwagon. This person’s title and placement within the organization may differ – she could be a “business analyst,” “data scientist,” or even an “Excel guru,” but the role remains the same: to provide the organization with new insight into various business processes based on factual analysis of statistical data.

Data analysts face challenges within their organizations as their role assumes a new form. We are quickly progressing toward a future in which analysts play a much more central role in daily operational decision making within companies. Their performance will be evaluated based on their measurable contribution to the company’s success and revenues, and their compensation will be adjusted accordingly.

From Retrospect to Real-Time Insights

With business intelligence and data analytics tools constantly improving, we now have the ability to crunch big datasets in shorter timeframes. Previously, these analyses were done in retrospect, with the goal of evaluating whether a certain move the company made was justified. Now the analysis can be done before any type of decision is reached. In fact, the analysis is the driving force behind such decisions.

For an analyst, it’s no longer enough to be able to analyze historical trends, the way business processes develop over time, etc. Today, data analysis is expected to provide real, factual-based insights in real time and to play an actual part in decision-making processes on every level. And the analyst has to produce actionable insight from the available data to justify his position at the company.

Big Data: Big Challenges and Big Opportunities

It seems that everyone is talking about big data these days. While much of that talk is more jargon than substance, it does reflect a reality in which datasets are becoming larger and more disparate than they have ever been before, presenting a considerable challenge to data analysts.

Take historical data, for example: Storing data is much cheaper today than it was 15 years ago. Storage space can be expanded easily and at relatively low cost. Gathering data is not resource-intensive because most of the process is done by automated software tools such as Salesforce, Skype, and Google Analytics. Organizations don’t actually need to devote a significant number of man hours to such a project. Hence, a modern company collects many more records compared with a similar company a few decades ago.

However, using this data gives rise to several issues:

The sheer size of the datasets makes them difficult to work with in terms of computational resources.

There is a lot of variance within the data, specifically because it is collected automatically and there is no single logic behind the way it is stored.

To perform centralized analysis on different datasets, one needs to find ways to connect between all these different sources of data – that is, to have some logical point of comparison between them.

These issues can make the data analyst’s role difficult. On the other hand, all this data can be a goldmine of insights if the analyst and the organization find a way to get a reasonable grasp on it.

Tools such as dashboard software are becoming more robust and powerful. They can be useful in data preparation and joining data from multiple sources, tasks that previously took up to 80 percent of the time in data analytics projects.

Once you manage to get an integrative view of the data, you begin to see the opportunities that lie within...

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