Digital transformation projects have a number of desirable objectives: increased efficiency, better communication and collaboration, faster time-to-market, and greater business agility are among them. But BCG research shows that 70% of digital transformations fall short of their objectives, often with profound consequences. And McKinsey research shows that 70% of complex, large-scale change programs don’t reach their stated goals.
There are many reasons for these failures, but two I will focus on here are “massive uncertainty, massive risk” and “lack of internal capabilities”.
Reducing risk and uncertainty in digital transformation
According to IDC, “global spending on the digital transformation (DX) of business practices, products, and organizations is forecast to reach $2.8 trillion in 2025, more than double the amount allocated in 2020.” So there are massive amounts of money being spent to digitally transform, and any time there’s massive spending, there’s massive risk. One way for corporations to address this risk is by making smaller, more coordinated investments and constantly evaluating success and progress as well as failures and roadblocks.
One area where smaller bets can be made that have enormous potential ROI is enterprise search, which when deployed effectively can achieve all the desirable digital transformation objectives. This is true in all industries, but especially true in industries where science drives the product - think healthcare, pharmaceuticals, and many consumer products.
Addressing the problem of dark data
Effective enterprise search addresses the problem of Dark Data, which plagues most companies and is a huge barrier to successful digital transformation. Gartner defines Dark Data as “the information assets organizations collect, process, and store during regular business activities, but generally fail to use for other purposes – for example, analytics, business relationships and direct monetizing. Similar to Dark Matter in Physics, dark data often comprises most organizations’ universe of information assets.” According to a recent IBM study, over 80% of all data is dark and unstructured.
When an organization’s dark data is brought to light, or made accessible to employees, the benefits are substantial, especially if that data is scientific in nature:
- Increased efficiency as duplicative research and experimentation is drastically reduced
- Better communication and collaboration, as research that was done previously can be more readily identified, analyzed, and discussed among colleagues
- Faster-time-to-market as “research-on-research” is accelerated
- Greater agility as a result of faster research and analytics and a better understanding of prior hypotheses and results
Note that the benefits of enterprise search can align perfectly with the objectives of digital transformation listed in the first sentence of this post.
So, if enterprise search is a possible winning path to digital transformation, how can “massive uncertainty, massive risk” predicament be overcome? By starting small and building on success. An enterprise search initiative does not need to be massive. It can actually be small and fast: It can be successful with a:
- Smaller budget; several hundred thousand dollars instead of several million dollars
- Small amount of data, say a terabyte to start rather than the petabytes of data most scientific organizations possess
- Departmental project rather than an entire enterprise so successes and failures can be clearly understood
- Smaller number of users rather than forcing adoption on an entire organization
- Narrower scope than an enterprise SaaS deployment, to prove the concept
Eliminating the need for internal capabilities with an enterprise search platform
If we can agree that a narrower scope makes sense, there’s still the second issue: in most organizations IT and software engineering resources are extremely limited. Further, most digital transformation projects, including enterprise search initiatives, require advanced engineering capabilities. It doesn’t make sense to embark on a transformation that utilizes older technologies. A successful transformational project will incorporate artificial intelligence and machine learning to accelerate the time-to-value and incorporate a flexible, adaptable architecture. Few organizations have excess capacity in AI/ML and two in five companies see lack of technical expertise as a roadblock to AI.
For enterprise search, however, the need for internal capabilities for a successful deployment can be minimized if not eliminated entirely. In a well architected solution, all the individual software pieces have been carefully constructed so that content can be ingested automatically, updated automatically, and delivered to users in a modern user interface. These pieces can include all the AI/ML components to create a superior search and research experience. No engineers required. Additionally (although this does require engineers), data that was previously “dark” can be integrated into proprietary systems and other third party tools.
Nebula: A developer-free enterprise search engine from ResoluteAI
It is entirely possible to make tremendous progress in a digital transformation program through a manageable, cost-effective enterprise search deployment. Nebula, from ResoluteAI, offers the benefits and features of a truly enterprise solution, but can be implemented quickly for departments, therapeutic areas, research teams, and other groups without a massive investment in time and resources, and the related risks.
Critically, Nebula can be deployed “developer-free”. Internal software engineering resources are not required and there are no professional services “time and materials” associated with Nebula. After researching the landscape for enterprise search tools, Nebula was developed to address the key shortcomings and offer a robust solution designed for commercial scientific organizations.
Nebula delivers all the value and functionality of an enterprise search solution, but offers specific, unrivaled benefits for scientific organizations. Content is ingested and processed from practically any internal data repository and then “magic” happens: previously buried institutional knowledge is now available to staff in a simple user interface.
- Content, regardless of format, is tagged and categorized using multiple taxonomies
- ResoluteAI’s proprietary tagging engine
- MeSH headings
- Documents are processed so that anything can be found inside
- OCR is deployed on handwritten documents
- Image recognition is deployed to identify plots, graphs, logos, etc.
- OCR is deployed on images so that a graph can be found by searching for the label of the Y-axis
- Audio files are transcribed
- Video files are processed using
- Image recognition on each frame to identify plots, graphs, logos, people, etc.
- OCR on each frame to find slides within a video file
- Documents embedded in other documents can be cracked open so all content in all documents can be indexed and processed.
- Content can then be analyzed with a robust set of tools and visualizations
Challenge and opportunity
Most organizations have “big picture” digital transformation objectives and discrete but pervasive dark data problems. In scientific organizations, solving the dark data problem will result in dramatic improvements in research efficiency. By starting with a narrower focus, the risk of poor results reported by BCG and McKinsey can be mitigated.
Artificial Intelligence (AI) has been recognized as one of the central enablers of digital transformation. Nebula from ResoluteAI applies state-of-the-art AI/ML to solve the enterprise search problem and help make noticeable progress on the path to digital transformation.
To learn more about digital transformation and how Nebula can help you, please email us at email@example.com or drop us a line using the link below.