Combining fragmented data sources to gain a competitive edge

Create revenue opportunities and transform decision-making with integrated data. Learn how with Hyper's 5-step framework.

In today's fast-paced business environment, organizations need more data sources that make it easier to see the bigger picture. Siloed data sources limit a firm's ability to identify new revenue streams and can inhibit product aspirations and development. However, with the right approach and tools, it is possible to combine these fragmented data sources and unlock valuable insights that can drive revenue growth.

This post will dive into an abridged version of Hyper's framework for combining fragmented data sources. Next, we will pair it with some potential use cases in the real estate market, an industry ripe with opportunity buried behind distributed data sets. We also have a great post here on my work with DropOffer showcasing how I applied this framework successfully in past client work.

Let's paint start by painting the picture. Your business is looking to introduce a new game-changing product or expand on existing features within your current product. The underlying problem your product/feature will solve depends on combining fragmented data from separate sources. Some of these sources may have publicly available APIs. Some will license their data, and other sources require you to get creative. However, one thing is for sure, successfully combing these discrete data sets has the potential to create a significant opportunity for your business.  

Hyper Digital Partners 5 step framework to streamline combining fragmented data sources.

At Hyper, we have developed a 5 step framework to streamline this process:

Step 1: Identify relevant data sources

The first step in combining fragmented data sources is identifying which data sources are relevant and required for your business and will provide valuable insights. Your target sources could include data from internal systems, such as customer relationship management (CRM) and enterprise resource planning (ERP) systems, as well as external sources, like market research and customer feedback.

Step 2: Clean and normalize the data

Once you have identified the relevant data sources, the next step is to clean and normalize the data. It starts by removing duplicates, correcting errors and inconsistencies, and then converting the data into a standardized format that is joinable and can be analyzed.

Step 3: Integrate data

Once the data is cleaned and normalized, it should be integrated into a single data repository or data lake, allowing for a centralized view of the data and making it easier to combine and analyze the data from multiple sources.

Step 4: Analyze the data

With the data integrated, it is time to analyze it to uncover unique revenue stream opportunities. Internally at Hyper, we use various data analysis techniques, such as data visualization, machine learning, and predictive analytics. The main goal of this step is to identify patterns and correlations that allow you to identify new revenue streams and make informed business decisions.

Step 5: Take action

Once your analysis is complete and you have some prescriptive direction,  it is time to take action! Either by launching new products or services, entering new markets, or improving existing offerings. The key is to act on the insights uncovered from the data and turn them into concrete business opportunities.

Combining fragmented data sources can help organizations uncover unique revenue stream opportunities and drive growth. By following our 5 step framework,  businesses can turn fragmented data into valuable insights that can inform decisions and support long-term success.

This practice gets more interesting when applied to specific industries. Here I will focus on the real estate industry, characterized by a wealth of data generated from multiple sources, including property listings, market trends, and demographic information. 

See This In Action. Data-Driven Disruption in Real Estate: The DropOffer Story.

Combining fragmented data sources allows businesses focused on the real estate industry to gain significant advantages through this innovation. Some existing applications include:

 Predictive Property Pricing Tools

Real estate companies can develop a predictive property pricing tool that provides accurate property valuations by analyzing property listings, market trends, and demographic information. Real estate agents, investors, and homebuyers can use these insights to make informed decisions and uncover new investment opportunities.

Neighborhood Insights Dashboards

Real estate companies can develop a neighborhood insights dashboard that provides a comprehensive view of a particular area by integrating data from various sources -such as property listings, crime statistics, school ratings, and demographic information. Real estate agents use this to help their clients make informed decisions about where to live, and it can also be sold to home buyers as a premium service.

 Real Estate Investment Platforms

By leveraging data from property listings, market trends, and demographic information, real estate companies can develop a real estate investment platform that connects property investors with investment opportunities. This data is used to identify undervalued properties, track market trends, and provide insights into the local real estate market.

The real estate industry is so rich with data that any "creative data-to-product solutions" that allow us to pick up the fragments will more certainly unlock new revenue streams.

The question for 2023: "When and how will AI impact this?"

Artificial intelligence (AI) has the potential to revolutionize the real estate industry by helping organizations overcome the challenges posed by fragmented data, including leveraging machine learning algorithms, natural language processing (NLP), and other advanced AI technologies, real estate companies can turn fragmented data into valuable insights that can drive growth and improve their operational efficiency.

Soon, inaccurate property valuations, lack of neighborhood insights, inefficient investment processes, tedious data entry and manual processing, and incomplete or outdated property information will all be obsolete. The differentiation process will be based on three factors: "who can effectively commercialize available technology," "who can quickly identify a product-market match," and "who has the strongest go-to-market strategy."

Find out how Hyper Digital Partners can help you rethink leveraging data to drive additional revenue and gain a competitive advantage in your industry - Schedule a meeting with Hyper to learn how.

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