Rise in Data Monetization
In the beginning, collecting big data was primarily done for the purpose of creating a better customer experience, from how services were offered, to the types of products being made. Big data reigned, and businesses were obsessed with how to collect it.
But now companies are realizing that data is one of their biggest assets, and a huge potential revenue generator. As a result, they’re looking for opportunities to leverage advanced technologies to monetize big data through its acquisition, analysis, classification and storage.
Think about the sheer volume of big data. Just figuring out how to analyze and store scores of customer data is no simple undertaking. A reliable and structured process is needed to actually figure out data’s value and potential – and those that figure out how to tap into that potential will reap the rewards. You can see this data monetization in action every time Amazon uses predictive or prescriptive analysis to suggest other products customers might be interested in based on their previous activity and purchasing behavior.
But data monetization just scratches the surface of data management trends and new opportunities to take advantage of in 2018. Here’s what to look out for as the year unfolds.
Focusing on Data Humanism
Just because there’s opportunity in big data doesn’t mean we necessarily know how to offer it in a usable way. After all, figuring out how to apply big data to our businesses and everyday lives is an emerging science, and it’s still a struggle for even the biggest corporations.
We’ve already gotten a taste for how data humanism has evolved with chatbots that can understand our questions and answer them. Think about a credit card company’s automated customer service prompts and responses when we call; modern chatbots no longer sound like robots, but rather like friendly reps who can understand what we’re saying.
However, data humanism is evolving with a growing need to turn big data into small, digestible, and usable forms so we can focus on quality instead of quantity. This could mean creating fine-tuned customer personas based on big data collection, to hyper-focus on what consumers really want and how to reach them in a way that resonates and serves them best.
In other words, the trends all point back to needing more clarity from big data. Businesses need to be able to quickly gather actionable insights from their data, not just acquire it. Just like consumers can call in and ask a machine about their credit card balance and receive a humanized response, big businesses need tools and processes in order to clearly understand the questions big data can answer, and the solutions it can offer.
Leveraging the Value of Machine Learning and AI
Machine learning, AI, and big data seem to be maturing together and innovating the very way we do business. And it all points back to how we can extract the most value from big data. More industries are looking at how machines can naturally acquire scores of data, learn from it, and apply it to help revolutionize how they do business and reshape the consumer experience.
Here’s just one example of how machine learning is being applied to better serve customers. HubSpot uses Kemvi DeepGraph machine learning and natural language processing technology, and integrates it into its content management system. The end result is a better way to understand trigger events so their team can pitch new clients and offer more value to current customers.
Then there’s the Domo platform. This business dashboard can pull data from Salesforce, Square, Facebook, Shopify and others to help businesses spot real-time trends. The business management software company also introduced Mr. Roboto, a set of new features for the platform that draws upon AI and machine learning, and actually offers suggestions and insights to decision makers.
With the help of machine learning and AI, tedious data tasks like manually creating metadata and categorizing data can be done automatically and with more precision. We’re no longer just collecting data without a clear idea of how to use it; AI can help direct how and why we’re collecting data, and where to apply it.
Accelerating Cloud-Based Business Innovation
Migrating over to the cloud was once a way for companies to easily back up and store data, which helped enterprises scale faster and more efficiently.
But trends in 2018 point to new ways the cloud could actually help businesses innovate and create new business models. Consider how Microsoft 365 F1 tied together some of its most popular tools in the Office 365, Windows 10 and Enterprise Mobility + Security bundles to empower over 2 billion workers. Their cloud-based technology helps firstline workers worldwide to serve as the first point of contact between a company and its customers with the use of a single platform. It’s no longer necessary to rely on multiple pieces of software and systems to serve customers.
Amazon Web Services also uses its cloud-based agility to empower companies and enterprises to deploy services faster while increasing their revenue at the same time. Companies can build their own business applications directly in the cloud to create a more reliable infrastructure and shorten their time to market. Amazon Web Services offers WorkSpaces, which allows companies to leverage the benefits of virtual desktops without the effort of deploying and managing them.
Prioritizing Data Protection
As part of data management, organizations must consider data protection. Big data comes with big risks for companies that don’t follow compliance guidelines and protect consumers. Some upcoming regulations along these lines may change the very face of how we do business. For example, The General Data Protection Regulation (GDPR) will become EU law in the spring of 2018, and will affect how data is processed even outside of EU borders.
The new law will impact global businesses and demand compliance from any company that collects data on EU residents. EU residents will have the right to ask for a copy of all data collected on them, and to ask questions on how and why it’s being collected and processed in the first place.
And it’s not just about being more careful with existing data and systems: Article 25 of the GDPR details how the design and creation of systems must have privacy woven into their very framework from the start.
U.S. businesses that are lagging behind in preparation for the GDPR could face trouble and large fines, which means there will be a need for systems and services that can help bring them into compliance – and keep them there. The increased demand for regulatory compliance from around the world will create big opportunities for emerging industries to step in and handle these pervasive data protection concerns.
Increasing Emphasis on Data Governance
The term “data governance” has received buzz over the last few years, but it isn’t always universally understood. Data Governance is defined as “the overall management of your business’s data, and how it’s accessed and used while maintaining its integrity and security”, as well as keeping businesses within any regulatory laws.
This means that data governance requires a reliable and trusted framework for managing data assets throughout their entire lifecycle. If it sounds complicated, it is. Companies are still figuring out how to create and scale their data governance to improve the customer journey and their businesses.
To put it mildly, data governance requires intense collaboration across multiple departments. How your marketing team is accessing and using data likely looks completely different than how your customer service reps are using it. The same goes for your accounting and HR departments. That’s why your company must employ a more efficient data management structure that can support your data, whether you’re looking for richer business insights or a way to ensure your suppliers and vendors are best serving your business needs.
Data Management will increase in importance
Data management isn’t going anywhere, and the demand for it will only evolve and increase as our digital consumption and data-driven businesses grow. The question is how we’ll take advantage of data management and apply it to stay competitive and compliant in the years to come.
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