The analytics angle

23 March 2018

Data management and business value serialisation can create a wealth of data, but the challenges of storing and maintaining the integrity of this data is no simple task. World Pharmaceutical Frontiers examines the best approaches to data management, and how it can add value to an organization through information sharing and employing analytics to guide strategic business decisions.

It's a difficult task, finding ways of protecting revenue and maximising efficiency, while adhering to stringent, yet essential, regulations. Quite the balancing act, it can create headaches for the most hardy of business people. Effective investment can be a key tool for tackling this challenge, but this, too, can be beset with difficulties.

Can one justify the price of overhauling a legacy IT system, or introducing new technology to improve one's manufacturing capabilities? With the rush of new compliance notices introduced to combat counterfeiting, this could soon become a matter of necessity rather than a choice. Coupled with the rise of serialisation – which many regulatory bodies see as a tool for combatting smuggling and theft – spending, and where to spend, is a big deal. So, how can companies make the right choices and stay ahead of the competition?

Tackle the threat

As serialisation and track-and-trace options become more mainstream, pharmaceutical companies should explore how these capabilities can help improve supply chain planning and operations, elevate patient and doctor engagement, and increase sales.

Or so says Michael Zirkle, associate vice-president at Cognizant Business Consulting, who espouses that high-value products, complex supply chains and dependence on multiple organisations for distribution expose the industry to threats such as counterfeiting, theft and illegal diversions.

According to the Pharmaceutical Security Institute, nearly 2,500 incidents of counterfeiting are happening every year. To tackle this threat and ensure supply chain integrity, global regulatory serialisation initiatives are well under way. Within the next five years, approximately 65% of the global market is expected to require serialisation in the supply chain.

While regulatory compliance remains a top priority, the availability of information about serialised products throughout the supply chain provides a unique opportunity to take a data and analytics-driven approach to supply chain improvements by enabling greater visibility and collaboration.

The best way forward

Sharing such information across an organisation through dashboards and metrics yields greater opportunities for growth and innovation. Knowing how serialised product information is used by company members demonstrates its impact and allows decision-makers to prioritise where their efforts must be placed. Metrics can also be used to monitor how frequently certain information is accessed and by whom, and whether certain dashboards aren't being used at all. Such information is invaluable in guiding work efforts, and can improve how serialised products are monitored and how such information is used. Knowing how this information is employed is just as important as the product knowledge itself.

According to Matt Kelly at, there are some key metrics one should look for when studying the dashboards for improvements, and this style of checklist can work for other aspects of business too:

  • Supply chain misconduct and checklist: keep aware of the number of third parties or business partners with unclear beneficial owners, or owners who are exposed; critical suppliers where no anti-bribery training or audit is included in the current contract; and new suppliers, who have been on boarded somewhat recently, where the due diligence checklist is incomplete.

  • Employee conduct: 'critical' whistleblower allegations, such as financial fraud allegations or allegations of retaliation; exception requests for travel and entertainment policies, perhaps segmented by geography or employee seniority; and compliance training completion rates – an evergreen metric suitable for any dashboard.

  • Regulatory probes: open investigations – perhaps by length of time open, but ideally something more informative like 'cases approaching final disposition'; potential damages or some similar metric to denote potential penalties; and metrics that categorise all regulatory probes – perhaps by geography, regulator or nature of the problem (environmental or financial, for example).

Within the next five years, approximately 65% of the global market is expected to require serialisation in the supply chain.

The concept of governance, risk management and compliance (GRC) can differ based on the organisation and the type of governance/risk management that is being addressed. Although each company may vary slightly, the fact remains that all organisations are required to put processes in place to ensure they meet compliance and mitigate risk. These processes are supported by the use of specially designed solutions that tackle compliance and risk, or that report on the progress, violations or constraints that occur to maintain these solutions.

One way organisations are starting to manage initiatives company-wide is by deploying dashboards that enable executives and decision-makers to see how the organisation is performing as a whole, as well as by identifying potential issues before they occur.

Analytics for success

Using analytics to help improve multiple areas of business – including regulation and compliance efforts – by tracking cycles is a sure-fire way to see where the ball has been dropped and inefficiency allowed to creep in. Also, measuring effectiveness and plotting all major business trends creates a clear picture of where such metrics lie in the grand scheme of things.

Analysis can also enable organisations to fulfil most general governance, risk and compliance requirements. Between a strong back-end data warehousing environment and front-end reporting and analysis, such processes can offer organisations the ability to:

  • monitor date and time stamps, which provides more information and can reduce wastage
  • enable secure access and supervise transactions, reducing fraud and increasing security
  • ensure continuous data quality
  • manage data relationships
  • identify trends and monitor performance
  • set, manage and maintain goals
  • collaborate with peers to share information
  • manage processes through data.
The most effective approach to building a model usually starts, not with the data, but with identifying a problem and determining how the model can improve performance.

Data-driven strategies

Big data and analytics have risen to the top of the modern corporate agenda thanks to the tight profit lines that have become the norm in many industries since the financial crisis earlier this century.

As the business industry continues to experience rapid change due to technology, it is together that commercial interests and analytics can transform the way companies do business by delivering the kind of performance gains last seen in the 1990s, when organisations redesigned their core processes with the advent of the connected marketplace through the internet.

As data-driven strategies take hold, they will become an increasingly important point of competitive differentiation. By providing the analytics and metrics to guide strategic business decisions, McKinsey Consulting, one of the world's foremost experts in this field, lists three areas where such advice is prudent to financial success and regulatory compliance. It says that choosing the right data is important. "The volume of information is growing rapidly, while opportunities to expand insights by combining data are accelerating," says a McKinsey spokesperson. "Bigger and better data gives companies more panoramic and more granular views of their business environment."

Getting the necessary IT support is one method that would help in accruing better data. "Legacy IT structures may hinder new types of data sourcing, storage and analysis," they explain. "Existing IT architectures may prevent the integration of siloed information and managing unstructured data often remains beyond traditional IT capabilities."

While fully resolving issues like these can often takes years, business leaders can address short-term big-data needs by working with the company as a whole to prioritise requirements. What this means is quickly identifying and "connecting the most important data for use in analytics, and then mounting a clean-up operation to synchronise and merge overlapping data, and to work around missing information."

McKinsey's other important point is to build models that predict and optimise business outcomes. Data is essential, but performance improvements from analytics models allow companies to predict and optimise outcomes. More importantly, the most effective approach to building a model usually starts, not with the data, but with identifying a problem – such as compliance and the huge data stream opened up through serialisation – and determining how the model can improve performance.

It all comes down to whether or not companies are willing to put their money where their mouth is. Can serialisation and big data be enough to make sure one is compliant with regulations? This is a big deal for many companies in the modern globalised world, and making the most of your IT systems to adhere to this is only sensible.

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