We live in a world driven by insights and decision making fueled by data and analytics. Information and technologies are so ubiquitous that no organization needs to be a digital titan (such as Amazon or Google) to compete in today’s world of business. Companies, large and small, are recognizing the need to utilize the full value of their enterprise data to obtain an essential competitive edge. Since business competition can be fierce, it is now, more than ever, necessary for a company to be able to adeptly appropriate data assets from operational, analytic, cloud, and big data systems.
Data Engineering is the catalyst and Analytics Applications can be the accelerant that fuel the fires of decision making that are at the forefront of today’s business world by helping users identify business problems and formulate data-driven solutions.
It is not enough to merely have a Data Engineer or an Analytics Professional on payroll—they must be part of an end to end program of bringing Data Discover, Data engineering, Data Science, then Data Consumption together. And even before taking the first step, it is necessary to “start with why” as in to identify the business’ pain point in terms of a well-defined need for a specific analytic insight. This identified pain point informs scoping of required data and development of a purpose-built analytic micro-application, providing the user with a complete data analytics model that offers data-driven solutions to address specific problems.
(Don’t) “Go Fish!”
Mining data from various databases in order to generate new information and insights must be a precise, focused, and well-executed endeavor. Don’t waste time and get left behind by going data fishing.
Efficient and effective Data Engineering makes utilizing data easy by providing the data discovery and integration layer within a modern enterprise data management stack, a data fabric. The layer provides companies a connected and business-oriented map that is customized to gather enterprise data. People across the business with data or analytic needs can use that map to explore, understand, connect and blend data into analytic ready datasets that combine any data from any system across the enterprise.
Data Discovery
Combining Data Applications with Data Engineering provides a solution composed of advanced data integration and preparation tools, semantic and graph data models, and data science techniques. The first phase, data mining, starts with accumulation of required source data from internal and third-party data providers or sources. Experts can efficiently explore and integrate data dictionaries and sample data as well as leverage keyword and use case centric search and filtering functionalities.
External data products add considerable value when brought into a single location. With such an ecosystem, data can be explored and accessed from traditional sources like Experian, Equifax, TransUnion, and Whitepages to emergent sources such as geospatial and satellite imagery from HouseCanary and HazardHub, to name a few. Non-native data streams simultaneously to measure the impact on the accuracy of predictions, alerts, and ad-hoc queries. Essentially, this is a sandbox environment to discover and manage a multitude of external data sources to enrich customer data analytics models.
Data Engineering
Next, the data is engineered into blended analytics-ready datasets, a process we call data engineering. In this framework, a data engineering platform drives the discovery and integration process which enables business users to find, connect, and blend enterprise data into analytic-ready datasets. Data Engineering accelerates delivery of bespoke datasets from months to days and allows organizations to integrate all their data, including structured data from RDBMS, or flat files and unstructured data automates the data supply chain, allowing organizations to execute sophisticated data integration pipeline and publish analytic-ready datasets to downstream algorithms, applications, data scientists or other data consumers in the business in a lights-out automated way. Automation of the process enables scale by making it simpler and faster for more analytic-ready datasets to be delivered to the business more often. Data Engineering makes analytics-ready datasets available ad exported to other file formats, including CSV, JSON, and XML or in graphic formats as appropriate.
Data Science
Data Science that renders datasets to generate repeatable analytic insights should be brought together through process, business best practices, agile methods, and innovation—all of which are needed for unparalleled analytics-driven results readily available and inherently scalable.
Data Consumption
In the final phase, data consumption, the business user is provided easy access to information and insights to assist in making strategic, data-driven business decisions. Today’s world is mobile, and Analytics Microapps deliver solutions that work across devices to provide users with secure access to the insights they need anywhere and anytime. With the use of microapps, users can access and utilize outcomes from across platforms and current databases. This process provides businesses the opportunity to increase revenue, reduce costs, and prevent revenue loss while remaining compliant with government and industry standards.
Conclusion
A combined Analytics Applications and Data Engineering solution solve business problems quickly with real-time data. These and other emergent digital methods and technologies have accelerated the pace of decision making and are moving from pockets of brilliance toward mass industrialization through approaches that operationalize data analytics one business decision at a time.
