Skip links

Why Single Source of Truth is Vital in a Data-Dominated World?

Jump To Section

Why Single Source of Truth is Vital in a Data-Dominated World?

Why Single Source of Truth is Vital in a Data-Dominated World?
SSOT provides a rich possibility in understanding the past, seeing the present and planning for the future.

Amidst the whole upsurge around DATA being the new oil and businesses trying to make the most of it through any technical means, we often forget the most important aspect about data, that is trust. Data flows into an organization from endless sources – there is enterprise data, customer data, social data, industry data and so on. Within the business itself, data is getting generated from myriad sources, processes and departments. But for business to be able to take a decision that is validated through data, first they need to be a hundred percent sure that it is data that all stakeholders can trust. Hence the need for data engineering to build a Single Source of Truth (SSOT).

As we move into a world of Digital Business, a SSOT becomes a crucial tool in the digital arsenal of a business to take faster decisions, infuse agility, and confidently go to market with data.

First let’s understand what is a SSOT. A Single Source of Truth is the central repository of an organization of all their data from various sources, from where several points of view can be derived and used for the success of the organization. The SSOT provides a rich possibility in understanding the past, seeing the present and planning for the future.

This also helps organization talk in a ubiquitous language using specific but common data points. This ubiquitous language also helps in driving a coherent culture in the organization and break down silos.

As organizations are becoming more and more agile and adaptive in nature, the decision cycles are reducing. This reduction in decision cycles has few challenges, like need of instant information, need to see across the organization from a birds eye view yet zone into a specific reference and understand the impact of that.

To help organisations drive these short decision cycles and still keep the long term goal intact, the need of a lot of information is paramount. But when information is spread across the organization in a siloed manner and in different systems it drives disjointed decisions and not a coherent one.

SSOT is a means to address this issue. It is a process of collating all information of different functions of an organization into a common place in a systematic and periodic manner.

Collation: Many organisations have invested in large to medium ERP solutions and consider them to be the back bone of their system. This is very true but this is not the SSOT.  These are operational systems which help drive the process forward on a coordinated way. The data generated by these systems needs to be extracted and brought into a single platform or a database which can then be correlated to other aspects of the organization.

The process of bringing data from multiple systems like ERP, PLM , marketing, sales, finance and management systems and storing them into a closely related structure is the collation part of SSOT.

Systematic: Data collation needs to happen in a systematic way. For example, a sales cycle may be tracked on a quarterly schedule while a production cycle may be tracked on a weekly cycle and a human resource tracking may be on a daily cycle. The process of understanding different dimensions of how the data is created and used and standardizing them is part of the systematic process.

This helps in data standardization and creates that ubiquitous language in terms of time, material, quality of what an organization works on.

Periodic: Finally, the most important aspect of the SSOT is periodicity. How frequently are we bringing the data to understand the ongoing changes of the organisation. How frequently are insights getting generated out of the data and primarily, how frequently are these insights and data being used by the organisation. These are very important aspects for the success of the SSOT and the company.

While we call it a ‘Single’ Source of Truth, there can be multiple SSOTs inside an organisation, but no two SSOTs can be the master for the same function or dimension of an organisation. For example a product master can be owned by the product team and a customer master by the customer team, but there should not be another product master maintained and managed by say the manufacturing team.

Sometimes, multiple category level SSOTs within an organisation can also be collated to a broad org level SSOT which synchronizes the information and produces high level insights.

A multi-layered SSOT paradigm can be adopted as a starting point, especially for complex systems, that can eventually mature into an overarching one SSOT for the organization. This approach aligns to Digital Business principles of achieving bite-sized outcomes that helps in easier adoption by people and process in an agile manner.

In any case, having a well-built SSOT allows the business with faster, better decision making, provides insights into end-to-end operations, and empowers the business to deliver enhanced customer experiences. All of this leads into creating value streams and driving growth in revenue, margins, and profitability of the organization.

Visit to read more-www.cxotoday.com

admin

admin

Latest Reads

Subscribe

Suggested Reading

Ready to Unlock Yours Enterprise's Full Potential?

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes