MIND AND THE MACHINE:
THE AGE OF
Taking Intelligent Action on Your Data
The Change Agent
AI and machine-learning technologies are transforming business intelligence platforms into powerful tools that optimize the decision-making process, create agility and drive the business forward.
Understanding Decision Intelligence
It’s estimated that the average adult makes more than 35,000 decisions per day. That may sound absurd, but according to a Cornell University study, we make nearly 230 decisions each day on food alone. When you factor in all of the subconscious decisions about what to eat, what to wear or what to watch on TV, those decisions quickly add up. That doesn’t even consider the cause and effect of those decisions that splinter into hundreds of microdecisions. The bottom line is that we are faced with a massive number of decision points every day.
For executive leaders, the number of decisions is likely higher, as they make decisions that shape company strategy, culture and vision. When do they pivot the business in response to disruption? How do they activate their digital transformation strategy? Often, those decisions must be made quickly, all while considering numerous potential outcomes and balancing risk with opportunity. But what are the components of such a decision? Peter Drucker said, “effective executives know that decision making has its own systemic process and its own clearly defined elements.”
of organizations have fully adopted AI and machine learning tools.1
In a Harvard Business Review article, Drucker defined six steps in the decision-making process including:
- Classifying the problem
- Defining the problem
- Specifying the answer to the problem
- Deciding what is “right,” rather than what is acceptable, in order to meet the boundary conditions
- Building into the decision the action to carry it out
- Testing the validity and effectiveness of the decision against the actual course of events
Drucker’s process has stood the test of time and is as relevant today as it was in 1967 when he penned the article. The only thing missing from his framework is data. That’s understandable since Drucker penned those steps before technologies such as artificial intelligence and data analytics were as pervasive as they are today. What does that mean for executive decision-makers today?
For years, organizations have leveraged business intelligence dashboards to help users make data-driven decisions. Unfortunately, often the analytics platforms are chosen to fit the data rather than leading with what the company is trying to solve for. Even with those tools, the user is left to interpret the data and draw their own conclusions. The sheer volume of data and lack of context provided can lead to poor decisions and less than ideal outcomes. That’s where decision intelligence comes in.
Decision intelligence is a subset of artificial intelligence that focuses on tangible business outcomes, combining human and machine inputs to arrive at the most desirable conclusions. By incorporating AI and machine-learning technologies, organizations are transforming their business intelligence platforms into powerful tools that optimize the decision-making process, create agility and drive the business forward.
Microsoft discussed the importance of building a business powered by data in a recent white paper, and we explore some of those insights here.
Harnessing the Power of Data
What are some of the benefits of a data and analytics strategy?
Microsoft: Data and analytics technologies are some of the most powerful tools available to organizations. They can transform a company’s decision-making capabilities and create entirely new business models, improve the customer experience and drive revenue growth. Improving employee experiences by reducing repetitive tasks and increasing clarity of purpose are just some of the benefits that organizations can realize from a powerful data strategy. It also drives positive change for customers by improving their interactions with your business. Organizations must elaborate a data strategy and fundamentally rethink the way they operate and deliver services.
How does Microsoft help support a company’s data strategy?
Microsoft: We help organizations to create and nourish a good data culture that helps its workforce to be smart, efficient and compliant with regulations. To create a culture in which data is securely and productively shared usually means transforming an organization’s existing operating model and use of data.
Effective digital transformation requires us to do away with data silos, instead freeing up information to enable new innovations.
What recommendation do you have for organizations as they attempt to harness the power of data?
Microsoft Many companies are still coming to terms with cloud adoption and its positive, if radical, impact on their business models. But as Microsoft CEO Satya Nadella explained in 2018, “tech intensity” is a disruptive, forward-propelling force that doesn’t stand still. AI and machine-learning technologies are facilitating the ability to continuously innovate and disrupt business models, enabling businesses to stay ahead of competitors. Organizations that fail to rapidly adapt and automate business processes risk relying on going to data teams for every insight or question.
Effective digital transformation requires us to do away with data silos, instead freeing up information to enable new innovations. We are already seeing AI and ML opening up a new world of insights, smart services and automated workflows. Organizations that embrace continuous change as a business opportunity can build rapid growth through these technologies. They can pool expertise, for example, and form technology partnerships based on shared and open data platforms to deliver previously unimagined results.
Read the complete white paper here.
TEKsystems leaders Devang Pandya and Ramesh Vishwanathan share their perspective on how the application of decision intelligence creates opportunities to transform the business with data.
How Decision Intelligence Combines AI, ML and Human Elements for Holistic Decision-Making
Data powers and informs future-facing solutions and helps organizations save money, increase revenue, reduce risk and improve the customer experience. Industry leaders across financial services, telecommunications, healthcare, retail and many others apply data analytics solutions to get the right data to the right people at the right time to make informed decisions. The problem is that once you’ve collected, mined and analyzed the data, you’re tethered to that rearview data process, which defines the decision-making. And the outcomes are predicated on decisions made by an end user, who likely lacks formal training in data analysis and has a limited line of sight into other business units. For example, the marketing team fires up their BI dashboard and the data reveals the best performing content. Marketing and sales use the data to launch a new customer engagement campaign. But if the procurement and fulfillment units are unable to fill customer orders, the campaign backfires, leading to frustrated teams and worse, unsatisfied customers.
Ramesh Vishwanathan Senior Practice Director, TEKsystems
The feedback loop is built into the decision intelligence framework. Your decisions are based on the measures you’ve put in place. You’re able to monitor them to see if it’s going in the right direction and then quickly course-correct as needed.
Decision intelligence generates a holistic view of the business, removing the need to validate information or track down different stakeholders before making a decision. Through the decision intelligence mechanism, underpinned by AI, you can now look forward, backward and sideways. Every potential action is considered within the context of the expected outcomes, augmenting the human elements to help make faster, more accurate decisions. The AI models augment the decision-making process, putting the power of AI into the hands of the business users.
That all sounds great, so why isn’t that every company’s reality? Ten or 15 years ago, organizations were bound by the limitations of the technology. Business intelligence tools were implemented with the promise of delivering fresh insights that could solve every business problem. Often the technology failed to live up to the potential and users struggled to take action based on the data. Today, technology is no longer a hindrance. You’re bound only by your agility and your commitment to transforming the enterprise. Data and information is combined with cloud platforms, AI and machine learning to enable citizen data scientists across the enterprise to make decisions.
IDC estimates that the revenue of big data and analytics will reach $274 billion worldwide this year.3
Take, for instance, the supply chain, which as everyone knows is a massive challenge. The power of decision intelligence provides real-time insights into your supply chain ecosystem. Front-line decision-makers can assess the situation and redirect supply chain requirements to different routes, vendors or partners. The business users are equipped to confidently make the right decisions and minimize negative impacts.
Faster decision-making, increased agility and cost savings are some of the ways organizations can leverage decision intelligence, but they’re also looking for ways to monetize their data. To fully monetize the data, there must be some level of data sharing or data exchange taking place. This is where the cloud poses a risk in terms of compliance. The cloud is one of the most secure ways to protect your data, but it’s how you’re storing data in the cloud that creates an element of data security risk. With the pressure to comply with privacy regulations, organizations are turning to the concept of data clean rooms. Data clean rooms allow for the secure sharing and exchange of sensitive user data.
Devang PandyaSenior Practice Director, TEKsystems
The information already exists; that’s not the problem. It’s about unifying your data with AI and machine learning to enable quick decisions. That’s the power of decision intelligence.
In the financial services sector, artificial intelligence and machine-learning technologies are transforming everything from credit decisions to loan underwriting to financial risk management. The management of assets and investments is well suited for decision intelligence technology. With a holistic view of a customer’s portfolio, financial firms can use AI and ML tools to run sophisticated scenario analysis to help clients find the best investment opportunities.
Morgan Stanley’s WealthDesk enables its financial advisers to have an integrated view of assorted portfolios so they can manage their client relationships more effectively. WealthDesk provides financial advisers with a single dashboard for all their financial planning, advice and implementation tasks. The platform uses decision intelligence to recommend the most favorable investment strategies for their customers. The platform allows Morgan Stanley’s financial advisers to see the likely impact of portfolio changes on a client’s investments before a decision is made. The advisers review and verify the recommendations from the platform before advising the customer.All information shared herein was accessed from public sources as indicated.