Big Data. Artificial Intelligence. Machine Learning. BDA. RPA. Considering the recent increase in investments in data and analytics, we should all be familiar with these concepts. But are we happy with them? In a recent survey by NewVantage Partners, more than half of executives say their company fails to compete on data and analytics. In many cases, the issue isn’t a lack of tools to analyze data — advanced analytics tools powered by AI/ML technologies are poised to lead software investments through 2022 — but challenges organizing and cleaning data so that it can be a high-quality input for these tools. In fact, poor data quality is not only limiting opportunities to generate revenue, it’s actually costing companies an average of $15 million a year. 

Take marketing, sales and product development teams that depend on customer analytics to inform better experience, products and services. If their data is messy, they risk damaging the company’s reputation, and can impact customer retention and acquisition. In other cases, poor quality data exposes organizations to major regulatory and compliance risks, leaving them open to major legal or financial consequences. 

It’s impossible to realize the business benefits of big data and analytics if your databases are messy and full of duplicates, if information is spread across multiple databases with no consistent taxonomy, or if data prep is a manual, time consuming and expensive process. Organizations are also sitting on a mountain of unstructured data (i.e. social media posts, emails, chats) that they can’t easily process, leaving a rich source of information behind, and potentially leaving their existing data incomplete or ambiguous.

So, how can organizations turn data into a revenue generator? By taking the necessary steps to integrate, dedupe and enrich data to transform it from raw to decision-ready. At 7Park, we approach data transformation in 4 key ways:

1. Data Matching: Use unique canonical references to match information across systems, eliminate duplicates and connect information to form a more complete picture of your data. For example, manufacturers and retailers can increase the efficiency of their supply chains by tracking product demand by consolidating and normalizing mismatched inventory across locations.
   
2. Data Enrichment: Enrich entries with as much detail and context as possible to expand applications, increase accuracy and generate more nuanced findings. Insurance and financial companies rely on data to screen clients, customers and employees to mitigate exposure to risk, but with incomplete data it is difficult to produce accurate, precise results. With data enrichment, companies can differentiate between entries and simplify deduplication efforts to resolve inconsistencies in a database.
3. Unstructured Data Processing: By its nature, unstructured data is diverse, hard to search, and difficult for machines that only understand rows and columns to process. But unstructured data (i.e. chat, social media comments, news articles) can be a rich source of information that yield new insights. Unstructured data, like client notes in a Salesforce instance, can be linked to a known entity in one database and used to generate new structured reference data that will enrich and resolve duplicates in a different database.
4. Human In The Loop: A.I. and machine learning technologies deliver tremendous productivity gains, and increase the quantity and quality of insights delivered to users. But A.I. and machine learning algorithms tend to be a black box with little transparency for the user. Keeping a human in the process for validating results ensure accuracy and confidence. 

From Cost Center to Revenue Generator
Poor data quality costs enterprises millions of dollars. Don’t let the challenge of inefficient data preparation prevent your organization from capitalizing on opportunities to generate revenue or reduce costs. Implementing an automated and intelligent data preparation process will significantly reduce your reliance on manual labor to complete repetitive tasks, while speeding up the search for, and analysis of, more relevant information.

To learn more and see a live demo of our new Data Preparation Platform, visit us at the AI Summit at booth #904 on Dec. 11-12  in New York City. If you would like to schedule a specific meeting day and time, please fill out the form below.