While many businesses have strived to make data-driven decisions, the last few years made way for a data revolution in every industry. Now, more than ever, businesses have the ability to harness deep, complex data from most of the software solutions they use. Despite General Data Protection Regulation compliance for international organizations, the majority of businesses can leverage customer data for a variety of improvements and innovations.
With the availability of data – and data complexity – businesses must know how to use data legally, understand and analyze insights, and discover applied uses to ensure the information doesn’t go to waste.
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 20 years ago.
Data complexity – or the measure of how complex data is – describes large data sets from disparate sources and needs many resources to process. Often, complex data comes from several sources, with each having a different structure, size, query language and type. Of the other different types of data (simple, big and diversified), complex data can be described as a combination of big data and diversified data.
The technological advancements that allow businesses to gather, store and access vast amounts of data result in the availability of complex data, which is collected both internally, during the business’s regular operations, and externally.
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 generated by the internet of things.
Today’s businesses need to analyze more data – and more data sources – than ever with the exponential growth of new platforms, online channels, software and other technological innovations.
Datasets differ by depth and software, so each set needs to be analyzed within its intended scope.
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).
To illustrate the breadth and versatility of complex data, consider a pharmaceutical company. If a department within the company wants to analyze every internal research activity about diabetes, there’s seemingly limitless information to compile – from genetics to insulin levels to patient demographics. Data complexity analysis enables the researcher to organize the datasets into a structured view of related concepts to visualize how they connect.
While this challenge exists across the organization, it could be particularly vexing for the business professionals, who typically do not possess a technical background.
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 looks to put it to use by promoting a more data-driven corporate culture.
However, this intensifies the difficulty for employees: Not only are they required to deal with much more complex data, but this requirement often becomes a critical part of the way their performance is reviewed. In this state of affairs, the nontechnical manager could feel as though he has not been given the proper tools to succeed – which is why data access is so important.
Here are several ways you can leverage data in your business:
Managers are often asked to present cold, hard data points to support their proposed decisions or to justify previous ones. In addition, a majority of executives in leading enterprises report that analytics is central to their overall business strategy. The approach makes sense, as there’s not much point in collecting copious amounts of data if you’re not going to use it when your organization reaches a crossroads. Data gives businesses the opportunity to make highly informed decisions for the best possible outcome – whether that end result is a better bottom line or a more inclusive corporate culture.
Feel-good, fluffy pie charts or upward-trending graphs littered throughout a presentation will no longer suffice: Today’s executives must truly show that their actions have had a positive, quantifiable effect. Evaluating data remains one of the top ways to convince teammates, department heads or even yourself to take action.
In business settings, use data when it helps to illustrate a point to your audience.
Granting access to data spans business departments and disciplines. Usually, the operations and IT departments have the most technical information, but investing in becoming a data-driven business means departmental collaboration. This may mean enabling access to new software, sharing reports or holding meetings to discuss concrete numbers.
Data should be accessible to the people who need it to perform their tasks and responsibilities. Often, marketing shares its data with other departments to illustrate business decisions. Research and development will share data with marketing or production to demonstrate what is realistic and feasible versus impossible to accomplish.
Each employee and department needs to understand the importance of data to change the culture from data-phobic to data-driven.
Cultivating a data-positive culture requires choosing the right company software, investing in security and leading from the top.
In choosing the right software, considerations for the technology include the following:
Accessibility makes all the difference in choosing the right software. The learning curve, especially to pull data and insights, should be gradual enough for every intended user to understand.
To establish a data-positive culture, ensure customers and employees understand how the data is encrypted and kept safe. Executives should model the use of this technology and data complexity to encourage the rest of the company to use it too.
As with most technology, data analytics continues to evolve. Establishing data-first business practices means a commitment to continuous education and improvement. Employees will learn how to use data in their roles, managers will learn how best to leverage data for important business decisions and teams will learn how to communicate their data needs to each other through collaboration.
Businesses that provide education and training on using data will benefit from companywide knowledge and a shared value of data.
Complex data may seem like an insurmountable challenge in business. Should executives give up on data altogether and leave it to IT professionals and business analysts, or should every manager be trained to be a “mini-business analyst” capable of understanding and producing insights from increasingly complex datasets?
The answer lies somewhere in the 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.
Across the board, avoid muddying data with inconsistencies, working off of multiple sources of truth and changing unique customer identifiers.
Today’s self-service data analytics tools certainly go a long way toward enabling the use of complex data by simplifying the process of combining multiple sources of data in order to reach new and unexpected conclusions, as well as presenting the results in attractive visual formats.
Both the line-of-business workers and their companies should take it upon themselves to move their organizations past spreadsheets and into the new generation of business intelligence software, while making sure that the selected tools will be able to meet the demand for rapid analysis of complex data, even by nontechnical users.
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 not be beneficial to train business users to perform these tasks. Instead, companies that want to get serious about data should realize that today’s big and disparate datasets require dedicated employees and the tools to help them prepare and analyze complex data.
The solution to the problem of growing data complexity in today’s data-driven enterprise is threefold:
Once the business side of a company is more familiar with the data, it will also be able to 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.
Additional reporting by Saar Bitner