TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

What Good Data Looks Like and How to Leverage It the Right Way

Good data means you're able to sift through the noise and quickly identify current and future opportunities relevant to your business.

• By Naveen Rajkumar
• October 10, 2017

The advent of search engines has made it very easy to find data in its raw form. Going deeper to find "good data" -- the data points that are actionable and support business goals -- is much harder.

Companies pay data providers to gain access to actionable data, and that access is expensive. Transforming raw data into actionable data takes intentional work on many fronts and very few data providers have mastered this.

Let's take a quick look at a few of the key aspects that make up "good data":

Comprehensive data aggregation: To compose a complete and actionable data set, you must scour many data sources. Casting a wide net and looking at a breadth and depth of data sources will help you assemble a comprehensive view of a data point. For example, full and actionable contact information for a person might be obtained only by marrying data points from different sources (email could come from one source, salary information could come from another, etc.)

Careful deduplication: Duplicates are a constant source of irritation in any data set. In the interest of being comprehensive, data providers err on the side of deduplication. Duplicate data leads to poor data hygiene and will invariably hamper any aggregation or analysis that is done with the data set.

Normalized and structured for easy consumption: Good data sets are normalized and structured in a way that ensures the end consumer doesn't notice a difference in the data set based on the data element's source.

Timely and enriched data sets: Some data sets have to be timely for them to be actionable. Sending a bid opportunity to a bidder three days into a 15-day bid cycle puts the bidder at a huge competitive disadvantage. Further, good data is enriched with data elements that make it easy to quickly identify relevant opportunities, shorten response time, and improve efficiency.

Ease of integration: Data sets that are standalone and don't easily integrate with your existing systems and workflows can make acting on the data tough. If you are dealing with a sales-related data set, you'll want to make sure it easily connects to your existing CRM systems, marketing automation tools, and workflows.

How To Leverage Good Data the Right Way

Identifying what good data looks like requires an understanding of how the data should be used, the decisions the information will help drive, and knowing the actions or processes that it will support.

The next question for businesses is how they can make that data work for their company (in the case of Onvia's clients, how they can make it work to support their revenue goals).

In most cases, key business functions such as sales and marketing, finance, technology, and data teams are already collecting data sets from their tactical and day-to-day operations. Although companies are increasingly fluent about how to combine internally collected data sets such as these that support strategic BI, big data, and analytics initiatives, they have a long way to go when it comes to leveraging data about the markets in which they operate.

Growth companies tend to rely on "good marketplace data" to help them with strategic planning as well as with tactical execution. This includes data types that are important for strategic planning such as information on the market, competition, sales (including historical awards and future sales opportunities) and productivity metrics.

For example, leaders in sales and marketing functions, including operations, can partner with top-tier providers of marketplace data and sales intelligence to understand which channels, regions, or territories to prioritize. Such information can drive meaningful business actions such as competitive monitoring and flagging when the time is right to unseat a competitor. These insights can also help inform when to invest in entering new markets.

Good Data in the Business-to-Government Market

The business-to-government (B2G) marketplace, made up of private companies selling goods and services to federal, state, local, and educational government entities, is filled with raw data that is fragmented and unstructured. That data includes past contract awards, future spending plans, contact information, and much more. That raw data can become even more complicated to find and utilize depending on the type of agency that is being targeted.

For example, capturing data on a few larger government agencies isn't an impossible task. Anyone interested in selling to the public sector can find information about contracting opportunities in New York City or Los Angeles. However, the most successful companies in the space don't just sell to the largest cities. They also want to find information on the thousands of smaller cities, counties, and school districts having a significant portion of the opportunities and dollars available. They understand that success means comprehensive visibility into relevant business opportunities across the entire marketplace.

Another common scenario in B2G is indirect sales to government in the form of channel sales through a dealer, distributor, or reseller network. With access to the right marketplace intelligence, strategic sales and marketing leaders can use data to monitor the success of their channels and identify when a dealer is selling a more competitive product line. Acting on such information can trigger important business conversations and translate into revenue.

For our clients (such as Ford, United Rentals, and Bank of America) that use data to grow their sales to the public sector -- and for proactive companies in most every industry -- good data means being able to sift through the noise and quickly identify current and future opportunities that are relevant to your business. Enterprises can find success with data when that information is curated, deduplicated, normalized, structured, and easily integrated into their already existing systems and workflows.