How to use data analytics to thrive in a crisis

The COVID-19 pandemic has intensified the urgency around digital transformation. The ability to quickly derive insights from data and use them to navigate the uncertainly is no longer a nice to have, but a matter of survival. At the same time, businesses are expected to do more with less and stretch existing resources further, ruling out expensive options such as building custom analytics products or hiring more data scientists.

In my recent webinar with B2E consulting, How to Harness AI Without Writing Code, I proposed two complementary approaches for business leaders to navigate this challenge and use analytics to thrive in a crisis.

The first approach is to use off-the-shelf analytics software instead of custom builds. Such software solutions cater to specific business functions (sales, marketing, finance, or HR), are often tailored to industry-specific needs and include built-in integrations for most common data sources. This approach lowers the cost of development and maintenance, reduces implementation time from months to weeks, and brings the benefit of vendor’s support with training and adoption.

The market for data and analytics solutions is mature and offers a broad range of options. For example, a company looking to add the power of artificial intelligence to its sales function will find 73 analytics and BI platforms, 24 sales data intelligence solutions and 15 sales performance management solutions on Gartner Peer Insights. (Gartner Peer Insights is a free resource, and it can also be used to develop a shortlist by comparing features and customer reviews).

To help my clients select the right solution for their business need, I ask these questions:

  • What solutions are your peers and competitors using to solve this challenge?
  • Who else in your organisation is tackling this challenge? What software solutions have they tried?
  • What are the critical capabilities for you (e.g. deep industry or functional specialisation, breadth of functionality, built-in integrations, or access to 3rd party data)?
  • Can you experiment with multiple vendors and products?
  • How will the new product fit within the current system architecture and your other IT and business transformation initiatives?

You are likely to find a suitable and cost-effective off-the-shelf solution when your business need is not unique or core to your business model. For example, a pharmaceutical company may not need to develop custom machine learning algorithms to support decision making for marketing and sales teams. As a lot of its peers have similar decision support needs, the volume of demand has been sufficient to trigger development of off-the-shelf solutions.

Example: Aktana, a platform that provides AI-enabled decision making support for pharma marketing and sales teams. More than half of the world’s top-20 pharmaceutical companies use Aktana to quickly evaluate mountains of data, extracting what is relevant and valuable at the time of decision. During coronavirus pandemic, Aktana helped their clients quickly adapt their brand strategy to the volatile market situation by reprioritising sales activities and keeping their sales reps informed about new government policies.

The second approach is to use modern data science and machine learning platforms that serve enterprise-wide business needs. These platforms are cohesive software applications that offer a mixture of basic building blocks essential both for creating many kinds of data science solutions and incorporating such solutions into business processes, surrounding infrastructure, and products. They help business leaders adapt to changing environment by deriving insights from data across the enterprise.

Data science and ML platforms no longer cater exclusively to expert data scientists; they also meet the needs of business analysts and line-of-business employees with intuitive, easy to learn and no code interfaces. Whilst making expert data scientists more productive, they also empower the people closest to the business challenges (business analysts, finance, or marketing specialists) to ingest, share and explore data.

The democratisation of who can explore data and create models will dramatically increase the number of people analysing data and, as a result, improve the speed with which a business can move. Increased analytics capability will improve business leaders’ understanding of both the external environment (e.g. the behaviour of clients, competitors, regulators) and their internal resources (e.g. products, services, employees’ networks, knowledge and skills), and help them focus their capabilities on meeting emergent customer needs.

Example: Dataiku DSS is a collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their data products. In February, it was positioned as a Leader in Gartner 2020 Magic Quadrant for Data Science and ML Platforms. For business analysts, it enables to prepare data via a powerful visual interface, create and share datasets, charts, and dashboards in a single click, and experiment with more advanced features (like machine learning). For data scientists, it frees up time for high-impact work by reducing repetitive tasks and making it easier to share insights and status updates with peers, stakeholders, and project managers.

The challenges businesses face are significant and varied, but the means to solve them are largely present in existing internal and external data as well as in the ideas, observations, knowledge, and networks of individual employees. Off-the-shelf analytics solutions and modern data science and ML platforms empower these individuals to put that data to work on an unprecedented scale, all while keeping the costs manageable.

We are in a historic period of economic and social disruption.  The IMF is predicting a 6.5 percentage point drop in global GDP for 2021. Coupled with uncertainty around vaccine timelines and coronavirus mitigation actions, it is clear that organisations will continue to face big challenges in the next few months. However, the companies that adapt to these challenges and harness the power of analytics now will thrive and become the high performers of the future.

About the author, Marina Borozna

Marina is a Director at Pragmata Consulting and has recently partnered with B2E to deliver data and analytics services to their clients. She helps complex global organisations derive value from their data by converting business challenges into use cases and identifying, sourcing, and deploying best in class analytics solutions. Her past clients include top-10 global pharma companies, healthcare technology solution providers, as well as oil and gas majors and financial services institutions.