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3 top data challenges and how firms solved them

Social Content Writer, IBM Cloud

The future that data experts and science fiction authors have long predicted is here. We live in a data-driven world. Once-distant technologies such as artificial intelligence have become part of our daily lives. More than half of business have adopted data science strategies, and customer experiences across industries are transforming for the better.

Despite the many benefits, data science has difficulties. Here are three major data challenges, as well as some examples of how organizations are addressing them.

1. Data from multiple sources.

In today’s connected world, information often exists in multiple forms across multiple platforms. This can make it difficult for organizations to analyze all these formats and sources in a way that is accurate, clean and cost effective.

A success story from AMC Networks

Because of the challenges of fragmented data, AMC Networks’ Business Intelligence team was spending 80 percent of its time evaluating audience data and only 20 percent doing actual research. This made it extremely difficult to uncover needed insights.

IBM Analytics helped AMC Networks reverse that statistic. Now the team spends only 20 percent of its time analyzing audience data and 80 percent uncovering insights that make significant impacts on the business. Decision makers across the entire AMC organization can pull meaningful insights from ratings and audience data in-house almost instantly, a task that used to take days or even weeks.

Read the case study.

2. Data security.

Some organizations understand the power of data but remain concerned about how analytics tools might expose them to security or compliance threats. This is especially true in industries such as government and banking, where the stakes of losing critical data are high.

A success story from Fifth Third Bank

At Fifth Third Bank, leadership saw the potential for state-of-the-art data science tools to optimize marketing analytics, but security and compliance remained a crucial concern.

The firm used IBM Data Science Experience to create a controlled environment for its open source technologies to do machine learning with highly sensitive banking data in a security-rich environment. This solution unlocked the ability to harness cutting-edge machine learning techniques without sacrificing security and compliance.

Read the case study.

3. Translating data into business insights.

One of the biggest data challenges organizations face is articulating data discoveries in terms that matter to the business. Data scientists often lack the industry domain expertise to explain their findings, while business leaders lack data science skills. This can make it hard to get the actionable findings that firms need.

A success story from Revelwood

By leading with business acumen rather than data expertise alone, analytics solutions provider Revelwood successfully uses IBM technology to help clients realize the potential of their information. With these newfound insights, firms across industries are fine tuning operational performance, building better customer experiences and lifting sales and revenues to new heights.

To replicate these successes, organizations can work with experts like IBM and Revelwood or adopt similar best practices internally.

Read the case study.

You don’t have to struggle with these data science challenges alone. Tackle your data science challenges with help from an IBM expert.