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Not All Data Is Created Equal

The challenges and opportunities in creating an AI-powered organization, and what it takes to become one

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Nearly everything written about marketing these days seems to refer to data in some capacity. This should come as no surprise given that data creation is skyrocketing and, as an IDC whitepaper reports, enterprises are expected to produce 60 percent of the world's data by 2025. For organizations and marketers, this explosion in data production brings both opportunities and challenges.

The data ecosystem is evolving, and the EU's General Data Protection Regulation (GDPR) and other potential regulatory changes are forcing the industry to take a deeper look at its practices. With consumers paying more attention to how their data is being used, the companies that will succeed are the ones that will invest in trusted platforms and champion a more transparent future.

IBM's CEO Ginni Rometty recently spoke with CNBC about how 80 percent of an organization's data goes underleveraged. For organizations that have started down the path to unlocking data with artificial intelligence (AI), the advantages, as noted in a 2017 Capgemini Consulting research paper, are clear:

  • Three in four organizations increase sales of new products and services by more than 10 percent
  • Seventy-eight percent increase operational efficiency by more than 10 percent
  • Seventy-five percent enhance customer satisfaction by more than 10 percent
  • Seventy-nine percent generate new insights and better analysis

While the advantages that AI represents to marketers are significant, transitioning into an AI-powered organization isn't without its challenges. Here's what marketers need to know about that journey and some best practices for getting there.

 

Challenges in Harnessing Data's Power

Marketers who effectively use data to inform marketing decisions are more likely to outperform their competitors in revenue growth, profitability, and innovation, according to a 2018 research report by the IBM Institute for Business Value.

Knowing this, why aren't more organizations and brands using the data that is available?

For starters, cloud and AI tools are the vehicles to unlock the power of big data, but with the pace of change in the industry as rapid as it is, many companies lag behind in establishing the proper technology and data underpinnings they need to set themselves up for success.

From an enterprise perspective, effectively capturing and using data requires cloud-based technology and sufficient capabilities to store, access, and process data at scale. Additionally, organizations need to be able to overcome silos and manage their data across the enterprise in order to unlock its full potential.

From a marketer's perspective, there are many tools to choose from that hold the power to help brands plan, target, and engage more effectively. However, there is no "one size fits all" solution. Today's marketers require a variety of disparate services to create and manage their campaigns. In fact, according to a 2017 Ascend2 survey report, 51 percent of companies say they are using more than 21 different marketing technology tools, yet only 9 percent of marketers say they have all the marketing technology they need and fully utilize the tools they have.

The second key challenge is around quality data. Even with the right infrastructure, technologies, and tools, the output is only ever going to be as good as the quality of the data input.

Organizations need access to multiple types of meaningful data sources, both structured and unstructured. (Structured data consists of things like customer files and sales data. It is typically numbers, dates, and words that are stored in a database. Unstructured data consists of things like social media posts and customer call center audio recordings; it is data that doesn't live in spreadsheets and typically requires AI technology to decipher.)

Organizations also need a mix of data from internal and external sources. For external data sources, it is imperative that companies partner with trusted platforms and are aware of their data collection and safeguarding policies. Specifically as it relates to digital marketing, an absence of industry standards around data collection and usage has given birth to the application of "bad data," which has had serious implications among brands that have used such faulty data. For instance, last year one company sent out a targeted promotion meant for men — to women. And they're not alone in falling victim to bad data. As Marketing Week reports, the Royal Mail Data Services in 2016 estimated that 6 percent of brands' annual revenues are lost to bad data.

The GDPR has further placed a spotlight on the lack of standards associated with third-party data. With businesses now facing potential fines and customer backlash for irresponsible data practices, many companies are reevaluating who they do business with and demanding transparency. Additionally, the industry is exploring a labeling system to help combat the use of bad data in advertising.

 

The Goods on Good Data

So how does one get to "good data?" The following are some critical best practices that organizations and brands should consider:

  • Look in every corner. Conduct a complete audit of the data that exists within an organization. Unstructured data can be a tremendous source of value, but it requires the right kind of technology to automate the process and uncover valuable insights. By understanding what is available, companies can then determine what data sources they need from partners.
  • Get data partners in order. Now is the time to be proactive and ask tough questions from data partners about where their data comes from and how frequently it is refreshed. Valuable partners will welcome the opportunity to educate about the value adds and differentiators that go into their data. Marketers should be sure to ask questions about the sources of their partners' information, whether it is inferred or first-party data, and how transparent those partners are willing to be about how they collect and control their data.
  • Leverage contextual data sources. In the new GDPR-compliant world, contextual data sources, such as weather and anonymized location information, are privacy-friendly data signals that can help brands identify customer-need states and how to respond effectively.
  • Create the right culture. To move an organization forward rapidly, marketers should focus on culture. Teams should reinforce the desire to be agile, ripe for collaboration, and flexible enough to take risks.

With big data comes great opportunity for marketers and organizations — but not all data is created equal.

By embracing the new data ecosystem, leveraging cloud and AI tools, and partnering with trusted platforms, marketers can take steps to help ensure that they are leveraging quality data and driving revenue, growth, and performance.

Randi Stipes (@watsonads) is the head of global advertising solutions at IBM Watson Advertising. You can email her at rstipes@us.ibm.com.


 

 

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