Info Sheet: Digital transformation: Are you agile enough to meet the R&D challenges?
Rigid workflows and slow decision-making lead to delay in your R&D execution from product to launch.
We’ve all heard the saying, ‘time is money’. Now, let’s put it into an industrials perspective: consulting firm McKinsey found that products that hit the market on time but went over their budget by 50% reduced profits by 4%. In comparison, companies on budget but late to market by six months earned 33% less profits.
That was in the late ’80s! Now imagine delaying in today’s hypercompetitive market. In this article, we’ll see how an agile data management system enables companies to make decisions faster, encourage collaboration and bring their product to consumers sooner.
Productivity and collaboration suffer from poor data management
The amount of data in the world is increasing fast – 2.5 quintillion bytes of data are produced every day. While it’s all well and good to have more data, how we use it, and making sure it can be used in the future remains a challenge.
Then, there are the disparate systems in which data are stored, and how tricky it is to find and transfer data between them when it’s in different formats. This outdated and unstandardized approach leads to inefficient processes, and companies lose valuable time.
Another issue with not having an adequate data management system in place is not
being able to share and collaborate on that data with partners. Sharing large amounts of data and translating that to actionable insight to make decisions fast takes investment into
supporting technology. But many businesses might not understand the implications of a poor data strategy or, equally, lack the business model to encourage such large-scale data sharing, impeding speed and innovation.
Delays cost companies millions
Experts say any delay in producing an innovative product shortens its peak sell time in the market and caps profits by up to 50%. Delays also give
competitors the opportunity to seize market share and secure customers who are not willing to wait another six to 12 months for the product.
Since product development is driven by data, information and experience, companies can make decisions faster by reusing the information they already have. However, this knowledge is often trapped in siloes in organization systems and even across different companies. Data is disconnected from related information, forming only part of the picture.
It can be difficult to access past knowledge and make sense of it without a certain amount of context, but traditional data management tools such as paper notebooks and Excel spreadsheets add inefficiencies of disparate, siloed data, increasing both irregularities and project timelines. So, agility is affected and a company’s ability to make quick decisions based on insight suffers.
Engineers, scientists and researchers need a centralized location to securely store and access data when they need it. Getting all the documents required takes effective communication and collaboration with partners.
Context-rich data can’t be built-in afterwards
Companies need a standardized approach to their data management to ensure traceability and harmonization in their R&D processes.
And implementing applications like machine learning and AI to analyze R&D data requires context-rich data to give it meaning. Without context and meaning, these applications can’t provide the hoped-for insight. Data must be collected with context from the start in order to safeguard its quality and integrity – it can’t be built in after the fact without going through an expensive and time-consuming data cleansing exercise. To ensure data sharing is easy and enhances communication, companies must implement cutting-edge R&D data management platforms with the power to manage, integrate and analyze data for future use.
A study estimated that in 2019, companies would invest roughly $1.2 trillion in digital transformation. While many organizations have deployed Fourth Industrial Revolution
technology such as Internet of Things (IoT), Big Data and robotics to achieve their goals and make their data AI-ready, others are facing blockers, including government policies, R&D funding and resources, a lack of digital maturity, and not enough or ineffective communication.
Improving the data environment means machines can read and search for data, as well as access it when needed. With this in mind, many companies are implementing data consolidation and harmonization initiatives so they can be confident in how data has been captured, calculated and stored. AI and machine learning tools will only deliver valuable insight if you have invested time in ensuring the data you are feeding those technologies is of high quality and under full version control. But there is a way to go to achieve that next-level insight companies are being promised.
Digital data initiatives facilitate open communication
As digitization accelerates innovation and ensures data management processes are more efficient, it also empowers companies in the industry to form flexible partnerships. Collaborative data sharing enables companies to better address consumer needs and make decisions with speed and agility.
In fact, enhancing efficiencies and productivity in R&D by implementing digital platforms such as data lakes has the potential to add $28-31 billion in value to the industry.
Companies that have the technological infrastructure to support faster decision-making can react quickly to competitors’ activities and have the flexibility to adjust their products to meet shifts in the market.
To encourage open collaboration between partners and customers, organizations must embrace automation, process mapping, and workflow optimization. The aim is to have all
knowledge and data management in one place, such as a data lake, which stores, processes and analyzes both structured and unstructured data in a single repository. This includes data derived from research, development, manufacturing, safety testing, patents, regulatory compliance and post-launch feedback from vendors and customers. Additionally, having all data accessible from a single portal makes it traceable, meeting strict regulatory standards with ease.
Data can easily be uploaded as it doesn’t need to be formatted beforehand; it is configured as required. Thus, it can be used in different ways. Data scientists, for example, would be able to find and access data quickly, even if they don’t know exactly what they are looking for.
To be agile, companies need certain building blocks in place: data management of both structured and unstructured data, optimized R&D processes, and data capture with rich context. All of these have an important role in ensuring data is ready and consumable by data lakes, and subsequently machine learning and AI tools, facilitating traceability of results back to individual experiments and sharing this data with collaborating partners. When all this feeds a downstream data lake, analysis delivers insight, which drives agile decision-making. Insight helps companies innovate, remain competitive and keep up with ever-changing regulatory standards. The right data management platform can support this initiative.
Cut project timelines with the right digital platform
In addition to reducing development costs and simplifying the process, a flexible R&D data management layer enables companies to use data-driven insight to bring products to market sooner, giving them a competitive advantage.
Storing data in the cloud is the easy part. The challenge is to increase its value by making it consumable, demonstrating its reproducibility and maintaining its integrity. On top of that, accessing and using the data requires effective communication and working in partnership to maximize benefits.
Companies need a central R&D data platform, where data can be searched, shared for collaboration and reported on. A platform which provides robust audit trails and traceability, automates lab processes which helps to optimize the product development lifecycle, and leverage the power hidden in your R&D data. Only then will you be able to exploit opportunities which others can’t see.