How Statistical Analysis System Technology Can Reduce Risk Of Businesses With Right Data

To continue business undisrupted in a complex environment, there is a need for an all-encompassing approach that will integrate people, technology, infrastructure, and policy into a unified proactive response to risks that accompany every business

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Organizations are in dynamic and volatile circumstances today: it is imperative for them to equip themselves with future-proof measures to tackle risks as they come. With the global pandemic, several other natural and manmade risks, it is safe to say that organizations cannot be overprepared for these situations.


Apart from external factors, organizations operate in an increasingly complex business environment. Risk may also emanate from sources such as technological disruption, data security challenges and even cybersecurity, to name a few. To continue business undisrupted in such an environment, there is a need for an all-encompassing approach that will integrate people, technology, infrastructure, and policy into a unified proactive response to risks that accompany every business.

Future-proofing business decisions

Typically, forecasting helps reduce uncertainty in critical decisioning, however it tends to be geared to increasing efficiency than covering all possible risks. Thus, for more secure predictions, companies rely on data and the use of models based on historically successful forecasting to reduce such business risk. Revising these forecasts by updating forecasting models with current data routinely and looking at multiple scenario analyses can help reduce this risk even further.

Companies may also use algorithms on quality big data sets to spot behavioural patterns such as temporal and geographical buying patterns, impact of upcoming events/ weather changes and so on. Such insight can augment business decisioning further and can prompt businesses to pre-empt action that would reduce the risk of churn, loss of market share and engagement by delivering customized and relevant communication to the customer at scale.

Statistical Analysis System (SAS) has been a pioneering platform across various private sector and public sector applications, augmented by an ecosystem of open source and other platforms in the market- whether to look at financial risk or to analyze shopping data and spot emerging trends, thus helping anticipate customer’s future preferences and staying future ready. Its AI technologies can support diverse environments and scales to meet changing business needs.

Empowered and tailored decisioning

Organizations across industries need to make rapid decisions to address changing scenarios including adoption of newer technologies, dynamic shifts in competitor landscape, and rapidly changing consumer expectations. Such fast-paced changes demand quick decisioning, which is exposed to the risk and uncertainty of heuristic decision making. Heuristic decisioning grapples with human bias and error, and often sacrifices accuracy in favour of passing quicker judgements, thus increasing the business risk.

The platform allows organizations to augment their decisions with data-driven insights and reasoning, thereby helping them embed transparency and trust into the organizational culture. While banks use such technology to make analytically driven decisions such as providing tailored financial advice, meeting risk and compliance mandates, Telecom, Media and Entertainment companies use them to improve their decisioning quality, and thus bottom line – by improving the customer experience, enabling preventive network maintenance and capacity planning, and mitigating subscription / dealer fraud and such other areas.

Governance and compliance

To maintain the quality of predictions, forecasts and decisioning, organizations must first maintain high level of data quality to make data work for them. Without a governance framework, companies run several risks – including ethical AI, non-compliance with privacy, financial & data residency regulations and national security policies. Lack of data accountability would also mean that data owners would be hesitant to share data across departments, thus preventing the organization from maximizing efficiencies.

By automating compliance management, organizations can reap a host of benefits from data governance tools in the organization’s operations. Some of these include easier reporting and monitoring, risk reduction through validation and integrity controls, robust audit trails, version snapshots and lineage information from within the same user interface.

By smoothening the ways of working, organizations can bring business and IT on the same page and be assured of the quality of data that drives any Advanced Analytics backed initiatives. All in all, while organizations continue their journey to find their footing and manage risks in this “new normal”, advanced platforms such as SAS can prove to be a valuable companion on this road by virtue of its diverse and comprehensive suite of solutions.