Today’s enterprises have access to ever-increasing volumes of data holding a goldmine of insights. However, enterprise data warehouses are faced with the task of storing and synthesizing increasing data volumes. Legacy platforms are neither easily scalable nor flexible and due to fragmented data in multiple repositories, enterprises lack the data agility needed to make accurate business decisions. The need for Enterprise 360 transformation has never been greater.
What is a single source of truth?
A single source of truth is a unified repository that contains a single authoritative copy of all critical data.
While siloed data residing in disparate sources makes it impossible to trust a data record, a single source of truth ensures a fully trusted data source that enables everyone in an organization to speak the same data language. Most importantly, it provides a clear direction to organizations and helps drive a higher return on investment (ROI) by providing an aligned view of performance from a data perspective.
How can enterprises ensure a single source of truth?
There are five main steps that lead to a single source of truth for organizations– connecting to disparate sources, data profiling, logical data model, analysis, and consumption. Each of these stages is integrated to ensure a holistic solution that drives immediate business benefits and delivers a measurable ROI.
Connecting to disparate sources
Most large enterprises have multiple data sources (RDBMS, MPP, big data, streaming sources, etc.) that need to be cleaned up and unified for seamless accessing and processing. This is done by making connections and bringing the diverse data together into a single catalog, so it can be easily accessed, analyzed and used in a variety of different ways. To achieve this, organizations need a solution that can leverage machine learning and other data science-based algorithms to make logical connections among attributes of the data, data entities, and records.
For data to be processed, it must first be reviewed and profiled. Data profiling includes reviewing source data, understanding its structure, content and interrelationships, and identifying the potential for data projects.
The process includes tagging, data quality assessment, metadata harvesting and management, collecting descriptive stats, identifying foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.
Creating a logical data model
This step includes the creation of logical entities in the system across existing entities, providing the ability to seamlessly query across disparate heterogeneous sources without creating data copies. A logical data model describes the data in as much detail as possible regardless of how they will be physically implemented in the database. It is a data model of a specific problem domain expressed independently of a particular database management product or storage technology but in terms of data structures such as relational tables and columns or XML tags.
Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names. It is primarily carried out for consolidation (gathering, storing, and combining a vast amount of information at a single place) and deduplication. More and more organizations today are adopting a proactive approach to data analysis. Proactive analytics helps businesses leverage precise insights to drive productivity and accountability across operations, processes, projects, and teams. By using proactive analytics with machine learning and AI technologies, businesses can significantly optimize IT costs, drive greater innovation, and elevate their service experience.
This step involves enabling business users to use the data by making it available for analysis and querying. The consumption layer consumes the output provided by the analysis layer. The consumers of data can visualize applications, business processes, and services. It can be challenging to visualize the outcome of the analysis layer. This layer receives results from the analysis layer and presents them to the appropriate output layer. The various types of outputs cover human viewers, applications, and business processes.
The case for Enterprise 360 transformation
Businesses relying on disparate data sources to arrive at big decisions may be risking costly errors and missing key opportunities. Trusting a single, authoritative data repository and speaking the same data language across teams and functions is imperative. However, the road to a single source of truth is filled with challenges, and businesses must start their journey towards Enterprise 360 transformation with a trusted partner and a holistic strategy.