Wikipedia’s analytics page defines analytics as the discovery, interpretation and communication of meaningful patterns in data. The page goes on to add that analytics can be applied to business data to describe, predict and improve business performance.
For companies to survive and then thrive in today’s digital age, building capabilities in data analytics should be a key component of business strategy and talent development.
Three major factors have totally changed the way analytics is done in the digital age:
- Technology – today’s technology allows us to store and manage tens of billions of records, and that too with sub-second response times. We can work with structured and unstructured data formats, all types of content (text, voice, video, images) and build stunning visuals.
- eCommerce and online presence – the eCommerce tools available today help us understand customer behavior with maximum granularity, at a lot size of one. And with a very high majority of our customers having some online presence or other, they are sending signals through the sheer volume of content that they generate.
- Collaboration – no company can go it alone in this digital age. We are seeing very creative partnerships in the marketplace. We see this in pharmaceutical companies’ investment in technology companies, tech-tech partnership, supplier-customer inventory-levels information exchange, etc. In this digital age there is greater acceptance of partnership and collaboration usually leading to exchange of data.
The one thread that is common to the three factors above is the huge proliferation of data. The popular “internet minute” graphic shows the volume of data that is generated by humans in a minute on various online platforms.
We are also on the cusp of an explosion of data that will be generated by IoT sensors.
Data is, arguably, one of the most under-utilized assets. Difficulty in analyzing data is also caused due to the state of the data (structured vs unstructured) and its (in)accessibility. A Deloitte report cites that more than 80% of the world’s data is unstructured. An older report from the Guardian, cites that less than 1% of the world’s data is analyzed.
The Impact of Analytics:
When analytics becomes mainstream, there will be huge benefits. This blog, from the Committee of Economic Development, details some of the impacts, including a $3 trillion-dollar benefit for just seven industries.
Earlier this year Roche acquired FlatIron for $1.9 bn. FlatIron provides oncology-specific EHR software to doctors and then analyzes the collected data for gaining real-world evidence for improving cancer treatments.
Previously in 2016, FlatIron and Foundation Medicine, Inc had launched a clinico-genomic database containing oncology-related information on nearly 20,000 patients. Here is an infographic on the database.
Recently Medidata, the leading platform for clinical development, has agreed to acquire SHYFT Analytics. This is intended to bring data science capabilities, research data, companies’ own CRM data and real-world data sources together on one platform.
We are aware of high business-value transformation stories of a floor cleaning chemical being repurposed into an effective mouthwash and Rogaine becoming a hair restoration treatment when initially conceived as blood-pressure medication.
With a systematic approach towards data analytics, companies can set up their systems, processes and personnel, to capture insights related to discovery of new products and more synergistic acquisitions.
Here’s how Life Cycle companies should think about Analytics
It all starts with a hypothesis, and then you begin the analytics process. i.e. collect all the relevant data, draw conclusions and decide whether the hypothesis is confirmed or refuted, and then rinse and repeat until you get a business insight.
This is what makes analytics a competence and companies will need to build such competency in their workforce.
But before that, they need to take a hard look at the data. Things to consider are:
- Identification, collection and enrichment of data
- Governance and compliance around data
- Legality around data, including privacy and consent management
- Building data analytics technological capability
- Building Data Analytics Expertise, including presentation of insights and improved visualization
Build a Data Analytics Center of Excellence:
To become a futuristic, digital enterprises, companies should build a center of excellence around data analytics.
Automated machine learning (ML) insights will propose resolutions on a routine basis (e.g. use shipping lane B instead of the regular lane A, due to civil unrest in a geographical area). These will get better over time, will become available with most enterprise applications and can be weaved into operations. As an example, SAP’s S/4 HANA has more than 35 ML use cases built into various functions.
Other, more complex problems, will require deeper, systematic examination of information. For example:
- Is company X or Y a better acquisition?
- Should we close this plant in the Midwest?
- What amount of price hike is optimal?
- Is it better to bundle our product with a supplementary service, or offer a negotiated discount to our customers?
Data Analytics is the key to creating a competitive edge and a futuristic Life Sciences corporation. Executive sponsorship is necessary to build a data-driven and analytic culture in organizations and in people, and to lead the corporation into the future.
The importance of such analytics to Life Science companies cannot be overstated.
A wheel-chair company in the Netherlands, that uses IoT and analytics to improve their products and patients’ lifestyles.
The American Society for Clinical Oncology (ASCO) has developed CancerLinQ, a database with over one million records on cancer patients, to help oncologists leverage analytics to discern matching patterns and prescribe better treatments.
With a combination of apps, smartphone, tracking device and an analytics platform, Roche is able to help physicians induce huge changes to patients’ lifestyles.
Data analytics is fast becoming commonplace. To become a futuristic organization, companies must systematically manage data analytics and ensure its translation into business results.
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