Many companies have set themselves the goal of becoming a “data-driven company”. Ultimately, however, the majority fail to generate added value from their databases: Alleged POCs in the field of analytics do not scale, technology platforms prove to be immature and, last but not least, the way in which the workforce works together at various levels in the company must be fundamental change.
In the case of the pharmaceutical company GlaxoSmithKline (GSK), a data strategy has proven to be a suitable means of avoiding the pitfalls and mastering the change challenge. The “Value Strikes” strategy was launched in 2018 and geared towards short-term priorities and the creation of added value. GlaxoSmithKline was able to reap the benefits just one year later, when the group received the CIO 100 Award in the IT Excellence category thanks to a series of use cases in the area of advanced analytics.
According to Jen Baxter, Senior Vice President Tech Strategy at GSK, the program showed that, thanks to a structured approach, the group was able to demonstrate the possibilities for the use of artificial intelligence and also to scale these solutions effectively: “We are currently scaling a few selected use cases to systematically roll out data and analytics via our processes and embed them in them, “explains Baxter. “Easier said than done – but we’re making good progress and learning new things every day.”
GlaxoSmithKline set up one of the most successful “Value Strikes” initiatives in the area of inventory. GSK’s supply chain analytics team rolled out a whole range of new analysis tools. These should provide information about the possibilities to reduce the inventory. The tools included a digital value stream map, a safety stock optimizer and an inventory corridor report.
“The supply chains in the pharmaceutical industry are incredibly complex,” explains Shankar Jegasothy, Director Supply Chain Analytics at GSK. “We wanted to use our data to make our end-to-end supply chain more transparent and then use predictive and prescriptive analytics to make decisions.”
GSK Analytics was also successful in the area of liabilities: New tools ensure that invoices and orders are always in line with compliance guidelines. They are also used for other purposes, such as upcoming contract extensions, to ensure best practices in all local markets and industries.
GlaxoSmithKline implemented both Value Strike initiatives within a few months and then began scaling them across business units. “Our approach was not to develop algorithms and technologies in isolation, but to solve our most pressing business challenges with the help of data and analytics. Each ‘Value Strike’ initiative was selected in consultation with our management – and key questions were identified which can potentially be answered with advanced analytics, “explains Baxter.
- Dr. Christoph Hönscheid, NTT Security
“You are successful if you have an overall strategy to protect confidential data. Of course, the EU General Data Protection Regulation is an unavoidable challenge that companies have to face. It can be an important impetus to take real action on data protection. But it is wise to look beyond compliance and regulations. An overall concept should firstly take into account legal requirements, secondly obligations towards partners and thirdly the company’s own interests in protecting its digital property. This is the only way to create a stable basis for using the appropriate technologies. This includes DLP, file-based encryption such as digital rights management or tokenization. A data classification that ultimately makes the decision via these protective mechanisms must be a supporting pillar in this overall concept. “
- Christian Nern, KPMG
“Basically, there are technical solutions or BI solutions to find out where the greatest protection needs exist in companies. Most important, however, is that the employees from the specialist departments are not only trained, but also know exactly what they are allowed to do with the data. This can be achieved much better by exchanging information about correct or incorrect behavior or by using example scenarios or subject-specific templates. In this way, one gradually comes into a quality or security culture that every company needs for Security by Design in order to apply AI in a targeted manner. “
- Marisa Parrilla, Horn & Company
“The cultural aspect must go beyond data governance and also take ethical aspects into account. Because not everything that is allowed according to the GDPR, a company should do. Data protection has a lot to do with trust and you don’t have to worry about creating this transparency externally. Rather, companies have to integrate both aspects into a data strategy and thus an overall strategy in order to achieve long-term competitive advantages from the data. “
- Dr. Jean-Michel Lourier, Lufthansa Industry Solutions
“When it comes to data protection, you have to distinguish between two things: security and privacy. While you are well positioned with the first, with the latter there is still a lot of uncertainty. Due to the vagueness of the GDPR, you often don’t know exactly how far you have to go to really be compliant – and that is the problem. This means that you always try to be on the safe side, which means that you miss out on many opportunities for data analytics. “
- Stefan Zsegora, Telefónica Germany NEXT
“If ten data scientists ask data protection officers at the same time whether what they are doing is okay, it will probably take two years to clarify that. Therefore, on the one hand, an environment is required in which the data scientist has use-case-independent legal certainty. For example, we have developed a special anonymization platform that provides precisely this level of security. On the other hand, certification bodies are required that transparently certify for everyone that what is done with the data is legally correct. Because especially in customer business you don’t stand a chance if there is even a hint of jokes in the air. “
- Dominik Koch, Teradata
“Data analytics and data protection are not mutually exclusive, they always go hand in hand. Data scientists must therefore be familiar with the general and industry-specific guidelines for data protection and data security. In order to know which data they are allowed to work with and which not, they have to be trained accordingly. To do this, they have to work closely with IT security specialists and be able to fall back on their know-how in complex cases. “
In order to transform the data previously stored in silos into insights, the data scientists at GlaxoSmithKline developed two platforms: A data provisioning platform integrates the company data in one system and continuously feeds algorithms with real-time data, while a visualization platform is used in each individual use case supports decision-making. Both platforms were set up in close coordination with business and end users.
“A significant amount of the work that went into the program went into groundwork,” reveals Baxter. “Business processes either had to be completely redesigned or adapted in order to harmonize with the new workflows. The tools used were not intended as permanent solutions, but as constantly evolving products that adapt to changing business requirements and thus add value for the future to guarantee.”
Every single one of the “Value Strike” projects was implemented with an agile, sprint-based approach that started with a Minimum Viable Product (MVP). The basic idea: to prove the
added value on a small basis, obtain feedback and then use this to significantly advance development and deployment.
The cross-functional team set up for this purpose, consisting of business experts, data scientists and technology specialists, as Jegasothy adds: “The various skill sets ensured that we could solve problems and obtain feedback more quickly. For example, we didn’t need a ‘perfect’ data set to generate added value – thanks to the combination of advanced analytics and data expertise, we were even able to generate insights from relatively poor data sets. “
The “Value Strikes” program provided GlaxoSmithKline with significant support in the first year in recognizing and using new business opportunities, says Jegasothy: “In addition to the resulting financial benefits, our planners and supply chain managers are now supported by data-driven insights.”
Jen Baxter has five tips for IT and analytics decision makers looking to help their companies get more value from data:
Business value counts: Data and analytics are not the goal, just enablers for business priorities. You should not only be able to articulate the individual measures that you take in a clear and structured manner, but above all also the way in which you generate added value for your business.
Comprehensive onboarding: Invest in the training of your teams and users and make sure that all new processes are documented.
Tools need trust: Complete transparency in terms of data and analytics is essential in order to equip the new tools with trust.
Prioritized development: An increasing number of feature requests shows that the tools are actually being used in the company. However, this can quickly overwhelm the developers, which is why the requests should be logged and prioritized according to business value.
Skills instead of tools: It’s not about static analysis, it’s about constantly evolving. This only works with the right people, the right skillsets and the right management. (fm)
This article is based on an article from our US sister publication CIO.com.