Data-driven decision making has changed the way businesses operate. Today’s enterprises have access to large volumes of data, which, if mined effectively, can give useful insights. However, as businesses grow, existing enterprise data warehouses face the challenge of storing and processing the increasing volume of data. Legacy platforms lack flexibility and scalability, and due to fragmented data stored across multiple repositories, enterprises find it difficult to analyze data across sources to make accurate and timely business decisions.
As traditional data warehouses fall short of today’s business requirements, enterprises are shifting to the cloud – on-premise, or hybrid big data warehouse environment. The need for Enterprise 360 has never been greater to eliminate the data silos and bring together enterprise-wide data to create a single source of truth across the business.
What is a single source of truth?
Single source of truth is a centralized repository that contains a single authoritative copy of all critical data. It is the principle of storing every data element only once and sourcing it from one single place. A single source of truth ensures greater data transparency, traceability, ownership, and cost eﬀectiveness. While siloed data residing in different sources and diﬀerent data formats 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 ROI by providing an aligned view of performance from a data perspective.
‘Single source of truth’ can also be understood as technology accelerators available to enable enterprises achieve a higher of efficiency and performance. These accelerators transform traditional systems like Teradata, Netezza, Oracles, Ab Initio ETL workloads, etc. to a Hadoop-based big data architecture where enterprises can perform various tasks like:
- Perform real-time streaming analytics with full Data360 coverage.
- Apply various data science techniques (machine learning , deep learning, AI, etc.) to derive valuable insights for informed decision making.
- Consume and visualize the data effectively for BI and reporting purposes.
Importance of having a single source of truth for enterprise data
Inconsistent data erodes trust in the numbers and impedes the ability of an organization to make informed decisions. To avoid the risk of making costly errors, enterprises must speak the same data language across functions and business units.
Some reasons why organizations today need a single source of truth are detailed below:
Siloed and fragmented data make it challenging to provide access to a single point of data-based truth for all members of the enterprise. Diﬀerent teams and business functions may rely on diﬀerent data sources and assume their version as the ultimate truth. Acting on diﬀerent data between departments can cause confusion, loss of productivity, and ultimately impair the overall decision-making process.
Loss of productivity
Multiple versions of data with no single trustable repository slow down day-to-day operations since validating and cross-checking every available dataset takes time. This often requires long waiting times for users, which may slow them down by hours or days in their primary task.
Lack of resource optimization
Data-related tasks done manually are barriers to business, both from a cost and resource standpoint. These tasks too often put firms at a disadvantage, affecting operational eﬀiciency, preventing timely decisions, and adopting innovations.
Challenges to achieving a single source of truth
While organizations realize the importance of breaking out of the siloed data environment, having a unified data model is not easy. Some challenges that organizations face to achieve a single source of truth are as follows:
Data losing its context
Extracting data from its correct context can render it meaningless. Organizations using data without knowing enough about it will lose out on critical customer insights and end up in a situation where the data will be inadequate to guide future business decisions.
Investments in legacy technology
The burden of legacy processes is hard to abandon, even for the most aggressive change agents. While the insecurities of a traditional workforce play a significant role, there has also been a historical fear of potential disruption to the business, and discomfort in moving to an advanced platform.
Disparate notions of data quality
The assessment of the quality of a data source is context dependent. The concepts of ‘good’ or ‘poor’ data are directly connected to the context in which the data is produced.
Consumption seems unviable
Most organizations are comfortable in their legacy systems and see a transformation as burdensome and disruptive. They envision new systems as unsustainable, impractical and diﬀicult to use. A change needs them to leave this comfort zone, revamp their business model, and proceed towards more eﬀicient business processes.
The stakes are high for today’s enterprises when it comes to decision making. 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.