Harnessing structured and unstructured data for digital transformation
A majority of organizations today are actively pursuing a digital transformation strategy. Reasons commonly cited include targeting greater flexibility, meeting evolving customer demands, expanding commercial opportunities, and lowering overall risk and cost.
As executives look to evolve, the common thread amongst these objectives is the critical importance of analyzing data – both structured and unstructured – to attaining lasting success.
The challenges associated with analyzing structured data and unstructured data differ. With structured data, complexity arises largely from gaining access to the information and with the volume of that data. Separating signal from noise is therefore on top of any data analyst’s mind.
Speed, cost and scale are also critical to ensuring you can act on the insights as quickly as possible – and there can be risk associated with moving data to optimize each of these three items. Moving compute to the data (vs. data to the compute) is a driving force in analytics strategies today.
With unstructured data, context is king. In the English language alone, the word “run” has 645 different meanings. You can run a company, go for a run, run into problems, and so on.
In order to uncover insights hidden within a “run” data string, you would need to first understand the context around the specific usage of “run.” Getting this right has downstream implications on how you classify, sort, and analyze your data. And of course, you will need to bridge data silos and understand information in different languages and formats to gain the best possible picture.
Enter artificial intelligence or machine learning, which has been used – somewhat narrowly, but in many cases with great success – in the enterprise for many years, but now is front-and-center as organizations digitally transform their business. The evidence of AI’s ascending role in the enterprise is substantial:
· 70 percent of customer engagements are expected to be driven by intelligent systems by 2022
· 58 percent of organizations are planning to integrate AI and analytics systems this year
· 49 percent of companies believe they are better able to meet customer experience expectations as a digital business with predictive analytics
Modern AI/ML strategies now cover a broader set of data, including structured and unstructured, and are being applied to more challenges than ever before. Likewise, there is an increasing number of enterprise tools available to help wrangle data. Consider the following applications of complex data insights, and whether your current tech toolbelt has the capabilities to transform your data into actionable insights.
Detect and Prevent Risk
Enterprise risk comes in many forms, and analytics are critical to address virtually all of them. Security operations (SecOps) and Intelligent GSOC (global security operations center) can benefit by automating the analysis across vast amounts of data — a task that would take SOC analysts months to complete on their own. With proven and targeted analytics, security teams can investigate real threats instead of testing hypotheses or chasing false alerts.
Boost and Sustain Revenue
Big data analytics has increasingly become a technology imperative to growing the top line. With access to even more data, such as sensor data from the Internet of Things (IoT), organizations are more successful in deriving accurate and actionable insights to outpace competitors by acting on unmet customer needs, under-funded parts of the business, emerging business models, and more. Personalized customer behavior analytics is one of the leading capabilities in this arena.
Drive Customer Engagement
Similarly, companies are constantly looking for new and better ways to engage customers at a reasonable cost. Organizations can eliminate intermediaries and employ digital platforms to reach and serve customers directly, closing the loop between data and action to better satisfy their needs. Cognitive search, knowledge discovery and ChatBot technology are all relevant in this space.
Streamline and Enhance Processes
IoT is creating massive volumes of sensor data with untapped value. But IoT analytics tools can deliver on the promise of predictive maintenance, smart metering, intelligent manufacturing, and more. Operations analytics ensures automated IT monitoring and remediation to reduce MTTR and operations costs. Legal departments can use predictive coding, or Technology-Assisted Review, to improve and streamline the process of reviewing billions of data objects for legal matters instead of sending each data object to an attorney to review individually.
Protect Customer Privacy
One of the hot-button topics in boardrooms around the world right now is protecting customer privacy. The EU General Data Protection Regulation (GDPR) was one of the first of dozens of regulations cropping up around the world. Analytics and security yet again come into play, as the sheer volume of information organizations are now required to protect demands a new level of intelligent classification.
File analytics and structured data management technologies play a critical role in identifying the right information to secure, and ultimately protect organizations from fines, sanctions, lawsuits, and more.
How you go about harnessing your information matters as much as the tools you seek to deploy. Make sure you draw a big enough circle around what you are trying to achieve, take a hard look at the technology you already have on hand to determine if you can further extend its value, and be sure to not discount the complexity and scale requirements you must address as you formulate your plan.
If you don’t already have the ability to analyze your unstructured data, find the tools to help you do so. Soon you will be harnessing your data to digitally transform your enterprise.