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Dizzying Data: What the Rise in Data Complexity Means for Business

Saar Bitner
Saar Bitner

At the start of 2016, it’s clear that data and analytics are playing a much more significant role in the enterprise than they previously had.

Organizations have faster and more direct access to data pertaining to their various operations, and constantly strive to apply data-driven methodologies in order to improve their performance across departments.

At the same time, the complexity of business data is growing, as companies look to analyze data from many new and disparate sources as part of their tactical and strategic decision-making.

This article will examine these two phenomenons -- the increase in data complexity and the growing demand for data-driven business practices, and ask what they mean for line-of-business (non-technical) workers and managers.

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More Data, More Sources, More Challenges

The first thing that needs to be acknowledged is that today’s data is much more complex than the data a typical business was dealing with twenty years ago.

Without going into too much detail, this is due to the technological advancements that now allow any business to gather, store and access vast amounts of data, both collected internally, during the business’s regular operations, as well as data from external data sources.

Alongside the size of the data, today’s data is often very diverse in nature and is no longer confined to spreadsheets, with various automated systems generating large amounts of structured or semi-structured data, as for example could be the case in machine data, social network data, or data generating by the Internet of Things (IoT).

This infographic summarizes research by the analysts at Ventana and the Aberdeen Group, which further demonstrates the fact that today’s businesses need to analyze more data and more data sources than ever.

The fact that data has grown both larger in size and more diverse in nature creates new challenges in the world of data analytics, as today’s datasets might each follow their own particular logic and require a deeper understanding of the way data is extracted and structured before any serious analysis can be done (again, when compared to the traditional spreadsheet).

While this challenge exists across the organization, it could be particularly vexing for the business professionals, who typically do not come from a technical background.

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Data-Driven Corporate Culture on the Rise

Complicating matters further is the fact that while data is becoming more complex to understand, organizations are demanding broader use of it in an increasing amount of business scenarios.

Once the data is available, senior management rightfully looks to actually put it to use by promoting a more data-driven corporate culture.

Managers are demanded to present cold hard data points to support their proposed decisions or to justify previous ones.

Feel-good, fluffy pie charts or upwards-trending graphs littered throughout a presentation will no longer suffice: today’s executive must truly show that her actions have had a positive, quantifiable effect.

In a survey conducted by Forbes and EY, a majority of executives in leading enterprises report that analytics is central to their overall business strategy.

This is very sensible from a business perspective, as there’s not much point in collecting copious amounts of data if you’re not actually going to use it when your organization reaches a crossroads.

However this intensifies the difficulty of LOB workers: not only are they required to deal with much more complex data, this requirement often becomes a critical part of the way their performance is reviewed.

In this state of affairs the non-technical manager could easily feel as though he has not been given the proper tools to succeed.

Knowing the New Tools of the Trade

What can be a possible solution to this conundrum? Should the business executives give up on data altogether and leave it to IT professionals and business analysts, or, conversely, should every manager be trained to be a “mini-business analyst”, capable of understanding and producing insights from increasingly complex datasets?

As is often the case in the business world and elsewhere, the answer is most likely somewhere within the golden middle: managers must become more data-savvy, and learn to understand basic concepts in data analysis and visualization, in effect, to be able to tell a story with data, and to dig into the relevant information and reach data-driven insights.

Today’s self-service data analytics tools certainly go a long way toward enabling this, mainly by simplifying the process of combining multiple sources of data in order to reach new and unexpected conclusions, and presenting the results in attractive visual formats.

Both the LOB workers themselves, and their companies, should take upon themselves the task of moving the entire organization past the spreadsheet and into this new generation of business intelligence software, while making sure that the selected tools will be able to meet the organization’s demand for rapid analysis of complex data, even by non-technical users.

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The Business Analyst is More Important Than Ever

On the flipside, these business workers and executives cannot, and should not be expected to, become actual data analysts themselves.

Tasks such as managing complicated data schemas, setting up automated ETL processes and ensuring data quality and governance are difficult tasks that are best left to professionals. The same applies to applying advanced statistical and predictive models for deeper analysis.

It would be neither efficient nor time-effective to train business users to perform these tasks. Instead, the companies that want to get serious about data should realize that today’s big and disparate datasets also require dedicated manpower, and the tools to help them prepare and analyze complex data.

Connecting to the Inner Analyst

In summary, the solution to the problem of growing data complexity in today’s data-driven enterprise is three-fold:

  1. Educating business departments to become more familiar with data and analytics.
  2. Implementing tools that enable business users to perform their own analysis.
  3. Tasking business analysts with the heavy-lifting of data modeling and advanced analysis.

Once the business side of the company is more familiar with the data, it will also be able communicate much more freely and easily with the technical side, while understanding what can and cannot reasonably be done with the organization’s existing data.

This will further promote harmony within the organization and pave the road to a brighter, more data-driven future.


Image Credit: Monkeybusinessimages / Getty Images
Saar Bitner
Saar Bitner Member
Saar Bitner is the VP of Marketing at Sisense Business Intelligence software, the leader in simplifying business analytics for complex data