In 2016, Experian Data Quality put out their Global Data Management Benchmark report, which laid out several insights on how enterprises are collecting, managing, and leveraging big data. Logically, some businesses have proven more adept at the task than others – using their data to improve decision making, customer experience, and internal processes. Others are struggling in the face of the digitally-driven proliferation of data.
In the report, Thomas Schutz, SVP, General Manager of Experian Data Quality elaborated, “a shift has taken place where businesses are using data for nearly every aspect of their organization" and that "the majority of sales decisions are expected to be driven by customer data by 2020." He further noted that “businesses get in their own way by refusing to create a culture around data and not prioritizing the proper funding and staffing for data management."
Not having enough data to accurately paint a picture of historical customer behavior, data patterns, or significant relationships between events, groups, or individuals is no longer a concern – but drowning in data may be.
The question then becomes, what is the best way to approach big data collection and analysis so that you’re effectively leveraging the data that you have? Simply letting it pile up, and never gaining the unique insights big data can provide a business is no longer a viable answer.
Even a team with best-class analysts can be overwhelmed by the sheer volume of data that enterprises collect today. The days of relying on Excel files to manipulate pivot tables to find patterns or correlations in your data have passed. Traditional approaches to data analysis may no longer be enough. Finding the meaningful signals within a mountain of data should be the aim of every enterprise, but a new approach may be necessary to do so. Nearly every business interaction creates actionable data. It is up to each enterprise to decide what they do with that information – let it compile and accumulate, or efficiently mine it for everything it has to offer.
A Better Way Forward
By effectively analyzing and gaining the intelligence that big data holds businesses can make more informed decisions to achieve better results. Information without analysis is not intelligence – but with effective processes in place, deep operational insights can be gained that otherwise would be lost in the noise.
To tap into the real value of their collected data enterprises need a data mining platform that finds, monitors, analyzes and summarizes the information needed to make informed decisions in real-time. These platforms act as a force multiplier of analyst man-hours, automate data extraction, and then aid in the identification of emerging trends and abnormal behavior.
How it Works
Big data mining platforms work by linking data sources from an organization’s own databases, visible and invisible web feeds, customer feedback, and proprietary data silos to make critical information more actionable in terms of decision-making and the insight it can provide. Using the latest machine learning techniques, these platforms can recognize trends and patterns in data, identify correlating factors, analyze sentiment, and find meaningful relationships, events, groups and individuals within amounts of data too large to be manually analyzed.
An ideal data mining platform then has the ability to turn its analytics into action with customized outreach strategies. It can escalate internal alerts, send targeted messages to customers, or deploy two-way communication to gather new data. For example, a platform that aggregates recipient’s preferred method and time of delivery, can help an enterprise more effectively reach a target customer with a custom message when the recipient is most receptive.
From there, big data mining platforms help internal teams to analyze numerical data for correlations, language for sentiment, and interactions for network relationships. Through an intuitive workflow, an optimal big data platform’s artificial intelligence engines can learn to filter data noise and mine media channels for the most relevant results. The platform tracks who’s talking, what they’re talking about, and how they feel, along with where and when reactions are generated. This use of internal data in combination with external sources can help enterprises better understand individual behavior and expected values and take action before problems reach a tipping point.
From businesses gaining insight on customer loss prevention, to governments targeting fraud, waste and abuse, automated big data analytics provide organizations the tools they need for real-time decision making. Effective platforms can enable users to discover, monitor, extract, analyze and visualize large structured and unstructured data sets in a multitude of formats and languages.
To conclude, it’s become increasingly important that businesses do not just collect data – but that they put in place the necessary platforms, processes, and people to actively manage, and effectively mine that data – turning it into real business outcomes.