How to Take Control of Your Unstructured Data for the Next New Normal

In the mad rush to adapt to the new normal imposed by the COVID-19 pandemic, many business leaders realized something about their businesses that is both enlightening and disheartening: They do not work efficiently.

No doubt, they knew this before — especially those that were left flat-footed when the move to the digital-first accelerated. But it took the disruption of the pandemic and the sudden requirements to be all-remote and to optimize for online for these companies to feel the weight of the dual anvils of inefficient processes and outdated technologies they have been carrying. 

Some business leaders now recognize that they must let go of these anvils if they are to thrive post-COVID — the next new normal. If you’re among them, you should revisit your tools, technologies, processes and people operations. The starting point for your upward trajectory? Data. 

Take control of your unstructured data.

Data is the foundation of a digital-first business. This fact was true before the pandemic, but it is crucial to understand in the post-COVID world. There is increased pressure on most companies to provide accurate, engaging content that’s ready for consumption — either directly or by web channel partners.

But many businesses aren’t investing enough, or at all, in their data. Their data remains hidden in dispersed files and legacy systems. And most of their unstructured data — which includes images, videos and documents — is hard-to-impossible to access as organizing has generally been a labor-intensive, manual process.

Related: Why Digital Transformation Is An Effective Crisis Response

This inaccessibility is unfortunate as these data elements can help companies create standout online presences that could compete with dominant ecommerce sites. Companies that can leverage their data can pivot from brick-and-mortar sales to ecommerce for the COVID-19 world — PepsiCo’s new direct-to-consumer offerings, e.g.

Invest in practical artificial intelligence and machine learning.

Another trend that works against efforts to get digitally ready is the postponement of artificial intelligence (AI) and machine learning (ML) projects, perhaps due to a misconception that these are science projects with uncertain or non-measurable ROI. 

But shelving such investments would be a serious mistake for many businesses. A new class of practical AI solutions has put data scientist-caliber power into the hands of business leaders and creatives. These users, or data citizens, can now model data and generate insights.

Practical AI and ML solutions can make businesses more resilient by helping them address these data challenges: 

1. Accessing data no matter where it resides — across multiple cloud storage file systems or in dedicated silos — and putting it into a structured format so it can be used in operations or analysis. 
2. Collecting all data so that everyone can efficiently and effectively work with it. 
3. Providing everyone the chance to learn from the data so they can create new analyses and processes that add value. 

Technologies like AI and ML can also help businesses solve another critical data problem: how to manage, and make the most of, rapidly growing repositories of unstructured data including videos and images. Using horizontal data found in various silos will be crucial for companies innovating from the inside out.  

Build workflows to do more with less.

Because of the explosion of crowdsourced content and the desire to reuse assets to maximize their value, companies need to prioritize AI and ML technologies. This is also why they should adopt a data-forward mindset. As you begin to understand your data better, you can automate more processes with better accuracy — and enable more efficient workflows overall.

It’s time for business leaders to think seriously about how they can harness and unleash their unstructured data. They need to surface attributes and data elements across repositories to make that content more accessible, usable and reusable. Then they can gain a complete picture of their data. These insights, in turn, will show them how well certain elements are performing so they can put these data elements to work for them optimizing customer experiences and outcomes. 

Related: Learn Today's Top Data Analysis Tools, Microsoft Excel, and Power BI with This $35 Bundle

As an example, consider a consumer packaged goods (CPG) company. This company uses AI and ML to surface and connect information about its products, formulas, packaging, campaigns, sales and more. With all these disparate data sources available and accessible to key teams such as product development and marketing, specific questions can be answered — including some that may redefine what an external-facing product is, compared to an internal-facing stock keeping unit (SKU).

Today’s buyers want to know not just the size and look of a product, but also what goes into it. They want to learn from a quick search if a product has allergens or organic ingredients. They also want to be able to search across products, regions, images and related categories. 

Vendors, like the CPG company in this example, are responsible for making sure all the correct product data — the current ingredients, copy in local languages, relevant images, videos of its usage and more — gets to these partners. Automating that process and using AI for the meta-tagging of the elements that define the product enable the rapid posting of product content to all channels. It also reduces manual intervention by the teams aggregating this information. 

The use of AI can reduce errors and rework by ensuring the right materials are surfaced to the proper channels and enriched with the most compelling and effective assets for each partner and set of markets. This is critically important for CPG businesses operating in the COVID-19 world that need to please their customers, work with online channels, and protect their bottom line — all while employing fewer resources.

Go ahead, disrupt your business a little more.

Business leaders should consider implementing any practical AI and ML initiatives that can increase their organization’s digital readiness. Wise business leaders will make these projects strategic priorities. And, for their companies’ long-term survival, these leaders should further disrupt the already upended status quo in their organization. 

Related: Making Machine Learning Accessible: 3 Ways Entrepreneurs Can Apply It Today

That’s a scary prospect in this time of uncertainty when there is a natural tendency to cling to what feels safe and familiar. But when company leaders proactively disrupt the status quo, they can then:

• Rethink and enhance internal processes, including the reduction of manual work, the streamlining of other activities and optimizing ecommerce operations.
• Improve how their teams work with the aid of automation and investing in AI and ML that can help reduce silos, improve collaboration, increase transparency and make data more accessible.
• Empower their people by equipping them with tools that can put the most accurate and complete data at everyone’s fingertips fast, creating a tech-savvy workforce staffed by highly efficient and productive data citizens who will drive innovation.

By taking these steps, business leaders can abandon those dual anvils of inefficient processes and technologies and position themselves for future successes. They can unlock the full power of all the data they already own and use it to surface new insights, make better decisions more quickly, provide personalized customer experiences and help their teams work more efficiently. By removing those obstacles that impede and frustrate their teams, they can unleash their workers' full potential — the key to thriving in the next normal.