Securely connecting the right data to the right technology is the key to proving innovation success. AI and ML can take innovation insights to the next level, adding new levels of intelligence to your program. However, we know that the risk associated with new technology can stifle innovation. Read on this article which includes a framework to launch data-driven innovation programs.
Secure Data Collaboration in Enterprise Innovation Strategy
Table of contents
Setting The Scene
The Data Collaboration Opportunity… and Stalemate
The Data Value Vs. Cost Equation
Understanding Common Data Collaboration Hurdles
A Tactical Approach: Crawl, Walk, then Run
Build or Buy? Choosing the Right Tech to Facilitate Innovation
Is Your Business Ready for Data Innovation?
Setting The Scene
As the data universe explodes in size to over 44 trillion gigabytes and traditional industries continue to be disrupted by digitalization, it’s no surprise that data collaboration has become the hottest innovation trend for 2020.
After all, the data opportunity is so ripe. IDC estimates that less than 0.5% of all data collected is analyzed and used.
Data collaboration is a process by which companies in disparate industries (e.g. financial, legal, government, and more) pool their vital data and extract value that can assist with decision-making and innovation.
In the emerging data collaboration sphere, we see enterprises partner with start-ups to fuel disruptive ideas and tech with rich data, governments work with private sector leaders to use data to develop ideas for smart cities of the future, and house-hold brands host hackathons to disrupt their business models through data insights. Though different, these projects share a common agenda; using data collaboration to accelerate innovation outcomes.
Over the past five years, we’ve seen the rise of the innovation executive who owns that agenda – a data-driven expert tasked with scouring the market, analyzing datasets, predicting trends, and investing in innovations that will put their organization head and shoulders above the competition.
The Data Collaboration Opportunity… and Stalemate
For many organizations, ‘playing it safe’ is the ongoing mantra in their day-to-day operations. But with innovation playing such an essential role in driving competitive advantage these days, data stagnation is no longer an option. Companies risk being left behind by not investing in the right technologies.
Business leaders who perfect their analytics programs can move the needle for company profits. Even small incremental changes can have a huge impact on the bottom line.
In financial services alone sharpening analytics efforts could lead to an increase in earnings of as much as $1 trillion annually for the global banking industry, according to McKinsey estimates.
And those benefits would be diverse – with approximately a third of those gains coming from “reduced fraud losses” and one-fifth from “better-informed pricing and promotion”.
Despite these figures and predictions, for many companies, particularly multinationals and global corporations, the idea of sharing data with different organizations to drive innovation is perceived as being too complicated and risky. Regulation plays a big role in this, and in the United States especially the California Consumer Privacy Act (CCPA) has set up swimming lanes for what companies can and cannot do with personal information, mirrored after Europ’s General Data Protection Regulation (GDPR).
The good news is that more than a dozen states have created their regulations off the back of the CCPA, which is starting conversations among business leaders.
But whether it’s Australian operations adhering to the Privacy Act, EU nations following the GDPR or US entities preparing for the incoming CCPA and state-relevant regulations, it’s ultimately up to innovation executives to sell those in the C-suite on not just the relevance but the need to adopt data collaboration, while protecting privacy.
The Data Value Vs. Cost Equation
Many organizations that aren’t investing in data collaboration say the reason is because of the cost – either monetary, time-based, or competitive. According to VentureBeat, only 13% of data science projects go into production, illustrating that organizations want to do more with data but aren’t able to get projects off the ground because of the costs involved. But data is such a valuable asset that not utilizing it to its full potential already leads to significant negative business outcomes. Loss of revenue due to missed personalization or co-marketing opportunities is just the tip of the iceberg when you consider the longer-term competitive impact of incomplete market insights.
The reality is that the cost of not sharing is far greater than the possible risks. And even these are minimal when companies jump into a secure data governance solution.
So, what’s the solution? It’s not a matter of convincing the C-suite that this new Plan B is better than their original Plan A and that they should ‘go all in’ on a holistic data ecosystem or collaboration strategy. Rather, innovation executives must encourage a process of internal readiness and progressive piloting. In short; crawl, walk, then run.
Understanding Common Data Collaboration Hurdles
Innovation executives can couch the ultimate transition to a data-sharing agenda in the metaphor that “the race has already begun”. That means if you aren’t participating, you’re already behind. An alternative is to define the clear business case, e.g. the need for your company to improve efficiency or the customer experience.
Most organizations face some hurdles when it comes to data-driven innovation. Answer these questions before getting started to ensure projects run smoothly:
1. What data do we have available and what are we allowed to do with it?
Enterprise organizations usually have vast amounts of data, but often siloed in different departments and legacy systems. Conduct an audit on the data assets available and who the various data owners are. This will provide a company-wide view of what is available and who is in charge. You may be surprised by what you uncover and the potential of the data sitting within your company.
The next step is determining what you are allowed to do with it:
- Does it contain personal information?
- Do we have up-to-date consent to use this data for secondary purposes?
- Does it contain proprietary or sensitive information?
The information gathered will also help to form the strategy and rules for data collection, storage, consent, and ownership in the organization. Learn from the current processes and improve them to be best-practice.
2. What is our data governance process and chain of command?
Defining a data governance strategy will lay the foundations for internal data collaboration readiness. Projects are typically stalled early on because teams do not have an agreed governance framework or approval chain to work with.
Consider these things in your data governance strategy:
- People and responsibilities
- Data policies and consent
- Data cleanliness and accessibility
- User access and permissions
- Tools and technologies used for analysis
- Processes for managing breaches or misuse
Building a data governance strategy should be an ongoing process that is across departments and functions. It will morph and change over time as the needs and data within the business change. Defining which people have the responsibility to make decisions about data collaboration projects will help the team know who to consult and gain approval from. The Chief Data Officer is the most fitting choice for overseeing all data projects, however, some organizations may not have this job function in their team right now.
3. What are the privacy and legal concerns?
Privacy must be a top priority for all organizations entrusted with customer’s sensitive and personal information, especially in a time where the threat of hefty fines for privacy infringements are being handed to some of the biggest companies in the world. Defining personal information and setting up a framework for separately personal information and other useful data will protect both the customer and your business. Each dataset has a different level of risk associated with it, depending on the level of privacy needed to manage the dataset. Changing the company mindset to consider each dataset individually will open up data in a new way while protecting the most sensitive information.
Legal can also be a blocker due to the inherent risk associated with multi-party data collaboration. It can be easier to not run a data collaboration project in the eyes of the legal department. However, then your organization will be missing out on the huge potential of data-driven innovation. Work with the legal team to set parameters and approval processes. Alternatively, work with a best-practice partner to understand multi-lateral data sharing agreements and project-based licensing.
4. What are the use cases and goals for data collaboration projects?
This is an important step: defining and prioritizing use cases for data collaboration projects. In some ways, the possibilities are endless, but it can be overwhelming to consider all the potential projects. To get started take an existing problem where you can demonstrate the impact of accessing new data, expertise, or tools. Marketing is usually a great place to start, where you have rich customer or analytics data. Consider working with existing business partners who might be good data collaborators. This can be an efficient launch project since commercial relationships like loyalty or co-marketing partners may already be in place.
5. What are the InfoSec and technology considerations?
Data collaboration can be a sore topic for InfoSec teams. Where data is stored is important and different for each organization. InfoSec teams do not want data to be at risk of being leaked, so work with them to understand their preferences for data storage and movement between teams and external partners.
Choosing secure technology can help alleviate the concerns around data collaboration security. Taking the InfoSec team on the journey and understanding their requirements early on will also help to keep the project on an upwards trajectory.
A Tactical Approach: Crawl, Walk, then Run
Most organizations find themselves somewhere on the data collaboration maturity curve.
The goal may be to fully operationalize data-driven innovation in your organization, but you have to crawl and walk before you can run.
As you build up your organizational experience with data projects, you will find that the use cases you are working on will get more complex. The risk profile, technical complexity, team skills, and partner involvement are all factors that impact the types of projects you undertake. To get started on your data collaboration journey take a phased approach that will set you up for success.
Crawl
A proof of concept (POC) is a great entry-level project. This may involve a small group of stakeholders investing in the testing phase of a data collaboration platform’s functionality, with internal teams to ‘prove out’ value due to a business need or vision.
Example: A financial institution conducts a governed data challenge with its own data scientists and cross-silo datasets.
Walk
Move into an extended project. This may involve a larger group of stakeholders engaging a trusted partner or solutions provider with the company’s raw data.
Example: A financial institution engages a machine learning (ML) company to test their solution in a governed innovation sandbox.
Run
Now we’ve reached the enterprise stage. This may involve an organization that’s already aligned with the initiative, and then the platform adoption is expanded to other divisions with various use cases and simultaneous projects.
Example: A financial institution’s marketing department runs a data-enrichment exercise with an airline (collaboration suite) while an AI fintech is being evaluated for investment or acquisition (innovation sandbox) and the organization is developing a financial consortium for shared insights within its ecosystem.
Once you can locate a customizable and extremely secure data collaboration solution, it’s crucial to drive a culture of data innovation without compromising governance. And, perhaps most importantly, your key team members need to get on board through the crawl, walk, run approach. Innovation should not just be the mandate of innovation or digital transformation executives. Creating a company-wide culture of innovation will drive competitive advantage and open up new opportunities across the organization. Having a central data office and technology suite that facilitates safe, secure, and scalable data projects will be the enabler for the whole company to have success with data collaboration.
Build or Buy? Choosing the Right Tech to Facilitate Innovation
Once enterprises have recognized the need to collaborate with data, the next question is obvious: should we build our system or buy into one? For global operations, they may decide the easiest solution is to put their IT department on the job.
The risk those enterprises run, however, is that they are essentially creating a silo for themselves. Yes, it’s a custom-made solution that will meet their current needs, but it’s a one-off, bespoke product that is the antithesis of what data collaboration is all about. There are two areas where building can create problems. The first area is taking a project view to data collaboration, instead of looking at the big picture. Having this narrow view will limit the potential of innovation in your organization. Innovation is not a one-off project, but a function that should have a long-term strategy.
The second area is the technical solution for managing data collaboration. Building a bespoke solution will only create a silo, making it difficult to replicate the process for other partners. To get long term value from data collaboration you need to work with multiple parties. Interoperability and a standardized process are key to create an operationalized data collaboration program for the future.
The alternative is to buy. By joining an established data collaboration ecosystem, business leaders can manage their projects and also discover a network of data partners that will drive new value to their organization. It also means innovation executives can create for themselves a scalable and – most importantly in terms of ROI – repeatable process.
Is Your Business Ready for Data Innovation?
Just as there’s no one-size-fits-all approach to data collaboration, so too is there no ‘right time’ to jump into the open data economy. Rather, innovation executives must work together with the C-suite and department heads to determine the what, why, and how of their proposed data collaboration initiatives.
It all comes down to a ‘compelling moment’ – and in many instances, this moment involves some organizational pain. That is, your operation has too much data (or not enough), you don’t have enough in-house analytical experts to derive value from your on-site data, your senior executives believe there is too much risk to invest in data collaboration – yet you see your enterprise falling behind competitors.
Once you recognize the need for innovation, there must be a true believer within the company willing to champion the cause of data collaboration. And perhaps more importantly, you need to know the proposed technical solution works and can scale with your needs.
Source: Data Republic
