Data in the New Year: Five Steps for Success in 2018

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inmobiThe numbers say it all: the big data and business analytics revenue forecast is set to reach $150.8 billion in 2018. This massive industry is growing at a rapid pace and it’s evident that companies understand that making data work is an absolutely necessity for success. However, there’s still a disconnect. We’ve never had this much data available to us – yet, we struggle with what to do with all of the information.

Organizations fail when they’re not able to operationalize insights that translate into an actionable strategy. In 2018, leaders don’t want to make those same mistakes. The following five realizations are critical to tapping into the transformative potential of data:

Understanding the Impact Machine Learning and Artificial Intelligence can have Towards Streamlining Business Processes:

Just a few years ago, machine learning (ML) and artificial intelligence (AI) were merely buzzwords and technologies that companies aspired to talk intelligently about and implement one day. Today, ML and AI are without question changing how businesses operate and transforming cost structures. By 2020, 85 percent of customer interactions will be managed without a human. With a greater emphasis on bots completing tasks that in previous years used to fall solely on humans, the data skills we can bring to every industry have never been more crucial. Humans will need to learn how to work in conjunction with next-generation technologies like ML and AI to process data and draw inferences from algorithms. For example, in the customer interaction use-case, ML/AI could provide the easy answers before handing off to a human for more nuanced queries. While technology will allow us to process data quickly in real time, critical thinking will become even more important.

Mapping Key Business Process Flows for the Company:

In order for a company to be successful with big data, there needs to be an increased understanding of the key business process flows constituting the company’s operations. For example, the sales process is well-understood, starting from lead identification and culminating in a report or shipment upon fulfillment of an order. Every single component of the flow needs to be mapped to identify opportunities for augmentation using ML/AI.

Consolidation and Creation of a Central Data Repository:

Far too much effort is spent in collating data for each use case. A lot of the heavy work around data sanity and consistency is often duplicated. Providing easy to use data can make the creation of insights and automation even faster. For example, the finance, operations, human resources and sales departments may have very different viewpoints and uses for big data. However, having proper insights into what different lines of business are leveraging big data for will create synergies. In too many cases, the data being collected is segmented by department, which creates silos. Without having visibility into other projects and priorities across the same organization, it creates a challenging situation and doesn’t paint an accurate picture of customer behavior.

Read More: Research Finds UK Sales Reps Lose Six Weeks a Year To Admin Tasks

Creating the Right Set of Tools:

It’s important for companies to organize a data sandbox – this is essentially a place where everyone can play with the same data in one setting. Open source software is one example where we’ve already seen results from a data sandbox. It’s crucial for organizations to design a system so data models are put through production and can scale with large data sets. At the end of the day, marketers don’t want to leave a customer hanging because they can’t process large data sources.

Defining Roles and Responsibilities:

More than 40 percent of data science tasks will be automated by 2020. With increased automation of tasks in data science that used to rely on human ability and skill, the roles that companies are hiring for will be much more niche. It will be critical to clearly define what each person at an organization is responsible for within the realm of data analytics. Since priorities shift on a constant basis, communicating roles and responsibilities leaves no room for mistakes to be made.

Tying these components together will generate the creative flux to bring about transformation. This transformation will set the company up for the new data-enabled age where machines work alongside humans to deliver value. Leaders will need to understand the key mechanics (business process flows) of their business, clean up their data, set up the playgrounds, and make sure everybody knows what the new rules are, and how to implement them.

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