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Is your organization poised to take advantage of the latest advances in analytics, artificial intelligence, and automation? Although more and more businesses are beginning to view their data as a competitive advantage, the consulting firm McKinsey has found that 85% of businesses aren’t using their data to its full potential.

The next wave of automation is being powered by many of the same factors that have driven the big data revolution: New sources and increased volumes of data, powerful tools (like Hadoop and Spark) that can quickly process that data and produce actionable insights, and advanced machine learning algorithms and neural networks that put those insights into action. How are each of these trends upending traditional business practices, and how can you use them to your organization’s advantage?

Predictive Analytics Gets Personal(ized)

At its heart, predictive analytics is about using historical data to make predictions. Long an important part of the financial services and consumer credit industries, today predictive analytics can be found in nearly every industry and function, helping organizations streamline their operations, maximize revenue streams, and inform decision making.

The same statistical techniques used to detect fraudulent credit card activity can also be used as research tools by medical and pharmaceutical companies. Similarly, sales and retail are using the cohort analysis to learn more about the people they’re trying to sell to, allowing them to develop personalized approaches based on demographic and behavioral data. Even HR departments are beginning to turn to analytics to identify high-quality candidates who might be overlooked by human screeners, reduce time-to-hire, and proactively address potential conflicts.

Neural Networks Let AI Learn from Its Mistakes

The advent of self-reinforcing neural networks has really changed the game when it comes to AI. What just a few years ago seemed like science fiction is now a rapidly growing part of business in many industries. It’s no surprise then that AI was the top growing skill on Upwork in Q2.

What separates this generation of AI from previous efforts is the generalizability of neural networks. With a structure inspired by the human brain, these types of AI are well suited to modeling high-level abstractions, which makes them immensely valuable for some of the toughest AI applications, like developing the computer vision techniques necessary for self-driving cars and facial recognition software. They’re also behind the quantum leaps we’ve witnessed in natural language processing tasks like machine translation and automatic summarization and sentiment analysis.

Automation Gives Analysts an Assist

Automation has been a major force in heavy industry and manufacturing for decades, where work tends to be repetitive and mechanical. However, the next wave of automation will take the insights generated by analytics and AI and apply them to a wide array of much more computationally intensive tasks. Specifically, McKinsey has identified data collection and processing work as the activities most likely to be disrupted by automation in the coming years. This applies even to fields and industries traditionally thought to be insulated from automation.

In the legal world, e-discovery software (powered by natural language processing) has allowed firms to automate some of the most time-consuming parts of the discovery process. Data cleaning and processing, once the bread-and-butter work of data scientists, is now largely handled by algorithms. These developments don’t eliminate the need for paralegals or data scientists, however. What they do is provide these roles with better, more reliable tools that they can rely on when doing the things that humans are still best at: things like interpretation, strategic thinking, and balancing competing priorities.

What You Need to Do to Be Ready

The next wave of automation is already underway, and the organizations that are prepared for it are likely to gain a competitive advantage. Here are three things you can be doing to best position yourself.

  1. Think strategically about your data. More and more firms are using data to optimize their business processes, but relatively few are using it to guide critical business decisions. At Upwork, we see many organizations turning to consulting agencies or freelance data scientists to help them identify the data they need to answer their most important questions.
  2. Integrate data into your business processes. At many organizations, data is its own team, and its operations are siloed off from other teams. To fully take advantage of your organization’s data, you need to incorporate data-driven thinking and practices into all aspects of your organization.
  3. Experiment, iterate, and scale. This can be hard for large companies with entrenched processes, but the ability to rapidly identify and respond to new opportunities will be critical to maintaining a competitive advantage. Your organization should be constantly experimenting, iterating on those experiments that succeed, and scaling the ones that have the potential to be high-impact.

The organizations that are able to move quickly and capitalize on new opportunities are the ones most likely to succeed. This is one area where freelance development teams may bring fresh perspectives and specialized skillsets, allowing organizations to quickly move on new opportunities without having to divert existing resources or spin up a whole new team.