Evolving the Data Solution Landscape
What does your next data project look like? What resources will be required to get the data ready for the application or analytic solution you are building? Do you have the IT staff required to set up and support the ongoing management of the data platform? Often, the time required to address these questions takes longer than originally predicted and the work of solving the business need that inspired the project is mired behind tactical requirements such as these. Because the number of data sources and the volume of data produced by those sources is growing, the challenge of data management is only going to increase over time.
Having access to a dynamic data platform will be essential to keep up with the onslaught of data from old and new sources. The scope of data required to obtain timely, deep insight into today’s housing industry is expanding
The proliferation of data generated by both traditional real estate documents and other taxing authorities, as well as more recent entrants to the scene, including Internet of Things (IoT) sources, along with numerous third-party sources, national and local levels of government, environmental, and administrative data such as permits and local use rules that contribute to the universe of data which directly or indirectly impacts the U.S. housing market. IoT sources are evolving rapidly and include home automation, utility telemetry, communication device wearables, and other web-enabled devices yet to even be considered
This documentation is intended for data-oriented readers, including data analysts, application developers, and database developers. It assumes that the reader has familiarity with SQL tools such as Microsoft’s SQL Server Management Studio or similar tools.
Data Solutions Success Factors – The Ability to Solve for “N”
What constitutes a successful data project? Often, success is defined as the ability to obtain actionable insights for your business by answering questions such as; where can I acquire my next customer or how do I run a more efficient operation? The application of machine learning to data projects has opened a new frontier of questions, shifting from looking at past results of the “who has” variety to the much more powerful predictive consideration of “who will”. The more sophisticated the questions and resulting predictions become, the more robust and flexible the data platform needs to be in order to keep up with not just the changes of focus during a project, but the data platform must be flexible to be able to accommodate the varying workload and facilitate access to the tools and people needed to execute the project. To be able to solve the questions in an ever-expanding landscape of data with more unknowns likely at the onset of an initiative, organizations should consider the following when building a solution:
1. Data is Not Enough: Data for the sake of data is nothing but digital noise. The business world is drowning in a growing tidal wave of data, but the value is realized only when data is applied as information for the business. The ability of the team to focus on the business outcomes of the project and ensure the right questions are being asked is vital to the success of the project.
2. Data Quality and Currency: Sourcing, loading, cleansing, and keeping data updated for an analytic solution requires more effort than is often realized. Having clean, updated data ready to go in the ATTOM Cloud database removes the burden of collecting and curating your own data.
3. Connect the Possibilities: The ability to integrate data sources seamlessly upon a platform to facilitate correlation between previously disparate data sets. This capacity is greatly enhanced by the functionality available on cloud platforms as machine learning has been democratized so that the power of sophisticated algorithmic processing is no longer only for the data science elite.
4. Collaboration: Whether internally amongst team members or across organizations, solving complicated problems can require a diverse team. Having a single workspace for the team to work in is a critical success factor.
5. Partnerships: Organizations are shifting from monolithic systems on their own infrastructure to applications and platforms hosted by technology partners.