Seventy percent of companies have reported minimal or no impact from Artificial Intelligence projects, according to a survey by MIT and Boston Consulting Group.

There are a number of reasons for this including a lack of focus on cultural change and training within an organisation as it adapts to new working practices, but the most important factor is poor data. This encompasses everything from inadequate data architecture and discovery, to modelling, quality, and governance.

If using the analogy that artificial intelligence is the “icing on the cake”, then data is the cake itself.

At some point over the next 12 months, with the global recession constraining budgets for every organisation, Chief Information Officers and Chief Data Officers will need to demonstrate a clear return on investment for their AI projects and provide evidence of measurable results.

To date, AI projects have been as much about showcasing that its mere presence demonstrates innovation and a digital transformation at the cutting edge of technology.  This will not be the case moving forward with increased financial and operational scrutiny so CIOs/CDOs will therefore need to “get it right” or risk funding for AI projects being curtailed.

Below are five data-related areas of focus that organisations should seek specialist guidance on to “get data ready” and enable success for these artificial intelligence projects.

Data Architecture

There is no more valuable role right now in any organisation than a data architect, and the best architects will understand that the end output from their role is to unlock value from data.

Data Architects understand the organisation’s strategy and business problems to be solved, but have the technical insight to get their hands dirty in the data itself. They know what data is needed and how that data is to be integrated by various systems.  They then set out clear guidance for everything from governance to security. This role is not the same as IT architecture and often organisations make the mistake of leaving this to the CTO or a traditional IT transformation provider, rather than bringing in the specialist support required to achieve success.

Data Access

Once the architecture is sound from a business perspective (rather than just an IT perspective), there are a huge number of data sources to feed the system and these will need careful management. The greater the richness of data from various sources the better the output, yet without management, an organisation becomes overloaded with messy, inaccurate, or incomplete data of different types and quality.

These data sources include enterprise data silos (in whatever format from databases to spreadsheets on someone’s hard drive), open-source data such as social media, government data, or data from the Internet of Things sensors.

However, the real challenge is that these are all separate data silos that cannot be moved into one convenient data warehouse for data scientists to extract, transform, and load. The growth of Data Catalogs demonstrate that they are quickly becoming a “must-have” for CIOs/CDOs in any organisation to solve the problem of multiple data silos with the more advanced data catalogs offering a discovery capability so users find data that they didn’t even know existed.

Data Modelling

Data modelling is often seen as boring and therefore overlooked but if your organisation wants to really get value out of its data then this is a critical activity. Why wouldn’t this be the case where data is now such a valuable asset in any organisation?

Time invested in data modelling ensures consistency of structure, terminology and standards throughout the organisation. Even the process from moving from a conceptual model to a physical data model encourages collaboration and agreement between all data stakeholders.

The data modelling process brings together business processes and aligns with data and IT communities. It will define key components and relationships between various sources of data and business workflows and will save time and cost for the organisation as well as improve performance. Put simply, it will enable everyone to understand how data is to be used within the organisation and, more importantly, how that data is turned into information, which delivers insight to an end-user.

Data Quality

Like any asset, its value depends on how reliable it is to the organisation.  The same applies to data – often organisations cannot or do not want to measure the cost to their organisation that can be directly attributable to poor quality data or missing data. Data quality is the standard to which an organisation’s data is accurate, timely and complete, as well as consistent with business rules.

Without good data quality, data itself cannot be relied upon for analytics or AI applications. This becomes even more important as the volume of data will grow, as will the types of data from different sources of data so this is an ongoing process that needs to be proactively managed through business rules and accepted by the entire organisation.

Data Governance

Having focussed on the above four items, data governance consists of the rules, enforcement and management of an organisation’s data as an asset. This ensures that the above four areas are not one-off activities that fall by the wayside once completed.

Data only has a certain lifespan for it to be useful. Good data governance accompanies the change in culture through training, which is required to complement AI projects.  Data governance must be a whole-organisation proactive activity to maintain a level of data access, quality, security and management to provide the organisation with data at the right time, and of the right quality, to turn into information, and therefore create data-driven insight on which business decision can be made.

A strategy that addresses how an organisation manages the above five areas of data management is a great start to demonstrate that an organisation is thinking about its data as an asset.  Artificial intelligence projects can then be far more successful in releasing the value of that data.

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Companies are realising that they can benefit tremendously from data and analytics to drive positive outcomes for themselves as well as customers. For this, organizations need to have a clear vision that focuses on driving efficiency, productivity and profitability. However, there are several challenges associated with data analytics which managers find difficult to handle.

Rahul Mehta, Sr Vice President and Head of Data Management Proficiency, CitiusTech talks about how tech leaders can effectively manage and deploy analytics function.

What Investments are needed to set up analytics functions in an organisation? How would one do the cost-benefit analysis?

An effective data architecture and technology infrastructure are prerequisites that must be in place to support any analytics initiative. They must also prioritize investments in analytics and start out on a small scale. Once a use case has been successfully piloted, organizations can expand its application across various enterprise functions to add more value, save costs, improve customer experience, market share impact, etc.

Organizations that have moved ahead from the pilot stage need to then focus on operationalizing and scaling their pilot models – some investment in training, process optimization and automation is anticipated at this stage. Overall, for the successful implementation of any major project, leadership buy-in is of utmost importance. Communication and direction from the top can go a long way in articulating the importance of initiatives. Senior-management involvement along with organisational structure play a decisive role in determining effectiveness of any initiative, including analytics.

What should tech leaders keep in mind while setting up analytics functions and while deploying it?

They need to understand and build aligned capabilities like data integration, data mining, enterprise data warehouse management, data lakes, data security, user experience management, etc. There is a strong interplay of these areas with core analytics functions like reporting / dashboard development, rules management and performance management.

Additionally, if we talk in healthcare context, healthcare, data models are lot more complex and adds to the overall implementation difficulty and complexity of performing analytics on the data. Hence, to get maximum value from the enormous volumes of data available to organizations, analytics teams will need to build a long-term approach that includes next-generation paradigms like AI, machine learning and deep learning. Identifying, grooming and retaining talent is likely to be an area of focus in establishing an analytics function.

Further, when moving from the pilot stage to the production stage, organisations will need to overcome challenges like data accessibility and availability, building, operationalizing and scaling models in a standardized way and making informed decisions around the right platforms and deployment options (on-cloud vs on-premise).

Lastly, organizations need to ensure that the quality of the data being analysed is sanitized. Gaps in data quality can impact data analysis and thereby lead to erroneous results. In the era where valuable, unstructured data pours in from various sources, fine-tuning data quality is an essential step to succeed with analytics.

How does analytics impact the top line and /or bottom line of an enterprise?

Investment of analytics can help exponentially increase efficiencies across business functions once implemented. Technology intervention can automate simple but time-consuming manual tasks, freeing up the individual’s time to work on more complex functions. It ensures accuracy and eliminates the probability of human error, ensuring a lot more accuracy in reporting and other such manual activities. Specifically, in the context of healthcare system, analytics has a huge impact on the providers’ and payers’ bottom line by identifying problem areas early on and use of artificial intelligence and machine learning algorithms to enable early detection and addressing of the issue promptly. It also helps organizations discover newer business opportunities and discern possible problems that enterprises could encounter in the long-term.

Though India boasts a large tech workforce there is a lack of data scientists and AI engineers who can create algorithms. How should enterprises deal with this shortage of tech talent?

Analytics today has become a multi-dimensional space, ranging from data mining and building data lakes at one end, extending to AI and Machine learning at the other. The range of skills required within analytics teams, therefore has become extremely diverse and demonstrating an organizational edge in analytics involves attracting, retaining, and sourcing the right talent. Analytics professionals need to have a strong understanding of specific domains and verticals in which they operate. It is tough both to obtain and retain employees with core analytical skills, even more than engineers and data scientists. We believe that one of the biggest challenges for companies engaging and keeping pace with technological changes, is acquiring the right people, with the necessary skillset and getting them trained to solve client-specific use cases.

Dealing with a complex and fast changing technology landscape, including new paradigms like RPA, AI/ML, Cloud, etc., means that analytics companies and the industry overall are under continuous pressure to stay ahead of the technology curve. Organizations that are limited in their ability to train and develop competencies are likely to face a bigger hiring challenge. At CitiusTech, we are addressing this through a mix of focused hiring, college/university partnership and internal learning and competency development via cross-skilling, up-skilling and multi-skilling programs across multiple areas such as data management, performance management, AI/ML, healthcare domain, etc.

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IT teams can salvage the value of legacy systems while pivoting to a new foundation offering better customer experiences.

CIOs and their teams are routinely maintaining costly, long-running legacy systems that have gathered a large amount of data about purchases, services, and customer behavior over the years. Sure, they’re getting a bit creaky in old age; but the most important problem has existed since they came online — each one was built in a vacuum and effectively siloed away from the others.

IT teams are feeling the strain of maintaining legacy systems at a time when customers are demanding a more tailored, robust, and streamlined experience from their organizations. Today, most customers want insights and capabilities to be accessed quickly and easily, whenever and wherever they need them, while receiving real-time updates and information through multiple channels (for example, web portals, mobile applications, e-mail, SMS). Because various older systems were one-off development projects that didn’t consider future dependencies or integrations, these legacy infrastructures simply aren’t prepared to meet the demands of the 24/7 digital economy without a lot of time and money being invested.

As a result, most organizations with legacy systems are losing revenue that their current offerings can’t drive or, even worse, are watching it get directed towards their competitors. New experiences that provide valuable insights and new capabilities are the best way for an organization to honor customer expectations and remain relevant in most market spaces. But here’s the problem: The data and logic necessary for these new applications is broken up across their legacy platforms, making it very difficult to access the full spectrum of information or the full range of capabilities needed to invent something new and valuable.

Fortunately, IT teams can salvage the value of legacy systems while pivoting to a new foundation. Core back-end scalable architecture supports quick and cost-effective new development without creating unnecessary technical debt. To enable those sought-after next-generation experiences, it takes a lean, thoughtful, and flexible service-oriented architecture that brokers access to both new and legacy information and services. Building this takes extra planning, talent, and effort that many IT teams aren’t ready to commit to, but here are a few key steps to make the process easier and ultimately more successful in the end.

Devise a strategy that allows legacy systems to participate in the new world. While it’s easy to imagine a new greenfield approach that will free your teams from legacy woes, it’s not realistic. Any solution needs to include an integration strategy for legacy systems that keeps them in the mix in the short term while eventually updating or replacing them when the time is right. Try to boil the ocean and you’ll get burned.

Ask the hard questions up front. IT teams must examine their current infrastructure and data assets to identify what needs to be changed to meet their objectives, then define the right technical architecture to meet the needs of the business. While it is tempting to focus on the immediate projects at hand, IT teams must consider the long-term initiatives that will follow in the months and years ahead. Systems, hardware, data, and personnel resources all need to be factored into the plan. To do so, they must ask themselves these tough questions:

• What are the problems you’re trying to solve for your business or your customers, now and in the future?
• What are the desired business outcomes that your technology would power or enable?
• How are you going to measure the success of your initiatives?
• What software, hardware, data, people, and other resources do you have in place now? Do they have the skills you need or should you hire new personnel or bring in outside help?
• What is the technology architecture you will need to facilitate the outcomes you’re after? To the previous point, are you sure that you have the right people in place to answer this correctly?
• How can you bridge the gaps between where you are now and where you need to be?

Once you have a better grasp of where you’re at, examine where you need to be from an overall enterprise architecture standpoint.  Consider a services-based approach that will drive business outcomes over several years. Here are some of the most important elements to consider:

Microservices.  A microservices architecture provides focused and independently deployable application components that fulfill three key objectives: development agility; deployment flexibility; and precise scalability. The highly granular, purpose-built nature of a microservice also facilitates a progressive migration strategy by adding to or replacing legacy components in smaller, more manageable pieces.

Big data strategy. “Big data” is so routine now that it’s better to just call this your “Data strategy.” New technology initiatives will usher in new data dependencies that you may not be accustomed to handling in your organization. As that data is processed and stored, you’ll need a consistent and rapid way to interact with it that will scale as your organization grows and the demands on your data architecture continue to evolve.

Security. Factor security into your plans from the start. You’ll want a straightforward security model that lays across infrastructure – covering data, service, and front-end tiers. Ensure that you have full customization of roles and permissions to account for the various types of users who will be working with your applications now and in the future.

Cloud support. This is also a good time to consider infrastructure flexibility. If you don’t have a cloud strategy, take a hard look at why not. Cloud providers, such as Microsoft Azure or Amazon Web Services, can provide instant geographical distribution, high availability configurations, elastic scaling, and several other useful services beyond the basics, for less money.

Production ready. Design your infrastructure with a focus on centralized monitoring, auditing, and continuous release. Your fancy new application ecosystem is worthless if you can’t manage it. You need to easily diagnose issues as they arise, and ideally have enough monitoring in place to recognize things before they turn into issues. Your system should tie notifications to workflows to prevent troubleshooting downtime and ensure that the right people are receiving the right information at the right time.

DevOps. You need deliver new functionality and fixes early and often in a reliable way, and for that you will need DevOps. Quality analysis, automated tests, packaging, and deployment should be a well-oiled machine with high levels of automation.

Lastly, be certain that your strategy achieves short- and long-term business objectives from the start, rather than doing it piecemeal. You’ll save both time and money by avoiding costly adjustments along with way. You’ll also preserve the sanity of your IT teams and development partners in the process. When you can show immediate ROI on your first initiative, you’ll develop a tail-wind of support from the business that will allow you to realize the long-term roadmap.

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