Data analytics, and the future of enterprise intelligence, offers tangible business benefits which will accelerate as its reach expands. This means dramatically improving the productivity and efficiency of knowledge workers, strengthening business processes to respond to customers faster and more accurately, reducing the time to launch new products, and improving the cost efficiencies of IT infrastructure.
All businesses are finding themselves gathering more and more data – that’s been a trend now for several years, and it has posed challenges for that duration. How to classify, store, and manage that data? What should be kept and what can be discarded? How is it affected by regulations like the Data Protection Law? And of course, how can it be mined to deliver actionable intelligence and business value?
There are infrastructural answers to most of those questions, but turning data into intelligence is an essential part of an organization’s digital transformation. With advanced analytics, insights can be gained and used to respond to the organization’s needs faster and more reliably.
Business expectations of intelligence
Virtually all CIOs place data science as a top priority for the coming years, with budgets for analytics and AI growing rapidly. There is an expectation that intelligence will directly drive business value.
As new technologies are brought online, such as the Internet of Things, the data being produced by an organization will escalate radically; it is therefore critical that the underlying analytics are in place and able to take advantage of the new data.
Coupled with enterprise automation such as RPA (robotic process automation) this creates a powerful combination; a transformed intelligence platform.
Some organizations are further down this road, deploying machine learning and artificial intelligence (AI) technologies to take their analytical capabilities to the next level, including predictive analytics capable of anticipating organizational or market needs in advance and recommend responses.
In Qatar, key industries for early adoption of these technologies included financial services, healthcare, and public sector service delivery. As the technologies have matured, they have become more accessible to organizations of all sizes, with rapid deployment and yield times.
In the current environment, healthcare is an obvious example of a vertical market relying on data and analytics to scale its service delivery, with high volumes of data gathered and processed to monitor, track, and predict the spread of COVID.
Organizations without a structured approach to data, and a clear strategy for analytics and intelligence, face many challenges. All these can be resolved, given skills, resources, and executive support.
Typical challenges include:
- A lack of skills to process, structure, and manage high volumes of complex data.
- Being unable to clearly define the business benefits of advanced analytics and AI
- Silos of data across enterprise applications
- Processes that are too slow to translate data into actionable intelligence
- Legacy infrastructure which is difficult to integrate
- Organizational resistance to the changes required in a data transformation
Some of these challenges are technological, while others are cultural. The latter can be the most challenging, since the right choice of technology partners can help develop strategic roadmaps to integrate and optimize the flow and storage of data to deliver business intelligence through analytics from the outset.
From business intelligence to business benefit
By investing in data- and decision-centric technologies, companies can transform business processes to take advantage in key areas:
- Take advantage of analytics to empower business decision making with more in-depth insights about the market, customers, and competitors
- Automate workflows by integrating the movement of data between processes, using intelligent process automation and business intelligence. This will greatly improve the accuracy, reliability, and speed of business operations
- Monitor and automate business and IT infrastructure to proactively remediate faults, optimize resources, and reduce costs
Ultimately, many of the processes described here will be empowered by the use of machine learning and AI technologies, often already in place within technology platforms as part of the management suite, but also readily available through on-premise infrastructure and cloud platforms.
AI tools can take in vast amounts of data and identify patterns, and the ways they map to business processes to deliver value. In time, it is likely that nearly all technologies will leverage AI in some way, starting from niche applications today to pervasive deployments in the future.
Security is a good example of where AI is delivering value, by taking in the high volume of events and alerts and developing models of behavioral analytics to identify malicious and accidentally damaging activities and even to take automated action to contain the intruder and mitigate any damage.
Roadmap to AI
The journey to enterprise intelligence powered by AI needs to be carefully planned.
Poorly managed AI adoption often leads to silos of intelligence within the business, each offering value to their stakeholders but ultimately failing to deliver the full business potential of a well-coordinated and integrated AI strategy.
All organizations have existing data silos, with varying degrees of complexity and maturity. Thoroughly assessing applications, workloads, and data will yield insights into the most promising use cases which can be developed and the optimal roadmaps to get there. Customers should be looking for quick-win use cases which yield business value, but which adhere to a strategic roadmap designed to add greater value as additional projects come online. Because the roadmap is so critical, make sure you select partners suppliers with the skills, technology, and experience to partner with you along that journey.
All organizations already have a complex mix of applications, platforms, and data repositories. Attempting to build a unified platform for all of it is certain to fail; instead, a strategic and focused approach is needed.
Starting with a family of related applications, data silos can be brought into a combined data lake without disruption. From there, processes can be analyzed and modeled, then structured with a data platform designed to deliver immediate value through insights and decision support. This should also be leveraged to establish the business case and to begin (or continue) the development of a data-friendly culture within the teams benefiting from the analytics.
Finance is a common starting point, with its relations to procurement, payroll, HR, and more. IT operations is another, focusing on systems management, user and customer experience, and SLA-driven performance management.
Even from a modest starting point, the key is to take that first step, establish a foothold for analytics in your organization, and build from there.
GBM supports many customers in Qatar in their transformation journey using IBM solutions and services. Contact us to discuss the ways IBM and GBM can help your business to develop a roadmap to leverage artificial intelligence and advanced data analytics to maximize business value. Visit these links to Accelerate your journey to AI with a prescriptive approach and gain Business Intelligence