Companies are besieged by information and bedazzled by IT solutions. With the rapid advancements in information technology, high-speed Internet, mobile technology and artificial intelligence, we now have access to huge amounts of data about customers, their demographics, and their online behavior across all touch points.
The advantage of access to so much information is not just increased revenue and long-lasting customer relationships, but also the ability to develop sensitivity to warning signals, which allow companies to prevent or mitigate disasters. The avoidance of conflict, the management of cyclical downturns or strategic missteps, and the management of the company’s future are at the core of creating intelligent businesses.
Companies have improved their practices with respect to capturing greater amounts of data. However, most organizations pass on these data to decision makers in bulk, leaving them to sift through it and identify relevant segments on which to base strategic and operational decisions.
Although they may have gotten good at collecting data, most companies have yet to develop the ability to generate actionable insights which they can quickly share with decision makers and clients.
Organizations need to focus on retrieving knowledge that is actionable, and to use effective processes and tools to share and take actions effectively. In other words, there needs to be an increased focus and on the analytics strengths and tooling of the modern company.
The concept of the intelligent company has evolved as a reaction to these new requirements. Massive amounts of data and rapidly evolving technologies conflict with our inability to adapt to the speed of change. An intelligent company is a company that ensures that the data it collects is quickly and continuously translated into actionable knowledge, and makes the relevant technologies part of its business model.
So how do we create an intelligent company?
For a company to shift towards becoming intelligent, it needs to have more than just the technology to enable the transformation. There is a need for significant changes in the way employees think about data and how it can be effectively processed and acted on, i.e. a change in culture and the way employees go about their daily business. In particular, data scientist Ronald van Loon has identified the following areas as key to creating intelligent processes that augment the abilities and efficiency of employees:
Design thinking is part of a broad methodology that amalgamates elements of imagination, intuition, holistic reasoning, and logic, to explore all the probable solutions for a given problem. It includes the identification of all unarticulated needs expressed by a consumer. After identifying the needs, the team creates solutions that address all those needs and end up creating the “wow” effect. The solutions are generated creatively and analytically. Design thing should always be more solution-oriented than problem-oriented.
Data is frequently used by organizations to find and extract information that can be used to assist or help in setting strategic goals. The efficiency and utility of these strategic goals go on to define the future of the organization and how it fares against the competition.
Once you understand the importance of data, it is easy to quantify the negative impact of low-quality information and badly structured data. In fact, bad data is estimated to coast global businesses in excess of 10 trillion dollars a year.
The data you are using should be flawless and should work in tandem with any artificial intelligence system deployed in the organization. Both AI and good data work hand in hand to assure the success of process analytics, especially predictive analytics. Business risk analysis, in particular, should always be based on meticulously curated and optimized data.
Successful organizations have been using data they gather from clients to follow them over multiple channels and to send them personalized messages and signals. Such practices may conflict with recently enacted data privacy laws, such as the GDPR; the intelligent company will manage such conflicts easily by making all levels of management aware of the challenges and potential solutions.
IoT or the Internet of Things combines all the technology and sensors gathering useful information, analyzing and storing it at the edge (locally) or centrally, to help in the optimization of the business processes and models. IoT and IIoT (Industrial Internet of Things) will impact manufacturing for years to come and change the way supply chains and logistics operate.
READ ALSO: The Future of the Internet of Things
Big Data Management and Analytics
Managing the data that organizations have is an integral part of digital transformation. Only after sifting through the hype and recognizing nuggets of insight and garnering a core understanding of the new business model will organizations be able to leverage these new competencies to their advantage.
Machine Learning and AI
Artificial Intelligence (AI) is a technology that works similar to the way our brains work. It tries to automate reasoning within certain boundaries. It augments humans and supports our capabilities to process a lot more data that we can otherwise. Its offshoot Machine learning will soon be the core of every enterprise.
Machine learning has a number of advantages:
Machine learning removes physical restrictions: If we only accomplish one thing by automating and digitalizing business processes, it must be the removal of physical limitations that restrict growth. Before the technological age, the biggest problem faced by businesses was operating within a limited space accessible only by a limited number of people. By embracing machine learning and diving into the world of e-commerce, for example, you don’t ever have to worry about running out of shelves. Data gathered from open source pools can improve manufacturing processes for all manner of operators.
Machine learning provides a deeper understanding of consumers: With the introduction of automated processes, businesses have become increasingly consumer-centric. Business owners need to understand the needs and wants of consumers.
If you do not deliver what consumers are looking for, there is a high probability that you will lose potential customers to the competition. Machine learning plays an important part in solving the mystery of consumer preferences. All required information is hidden behind the data accumulated by the business. You just have to crunch the code, and voila—you know what your customers are actively searching for.
Machine learning boosts efficiency: Machine learning solutions offer the opportunity to automate hitherto unconnected processes in the enterprise. Recent advances in algorithms, deep learning, and more efficient use of data have allowed machine learning to boost anything from legal research to medical diagnosis.
Data governance is a concept similar to that of data management. With the rise in hybrid data management solutions, the accessibility of data for repeated usage has significantly increased. Data quality and communication gaps can hinder the decisions taken as data flows through the organization.
Initiated through the concept of cryptocurrencies, blockchain is indeed the future of how information is distributed between parties. Smart contracts are one interesting innovation that they bring. Smart contracts can enhance the utility and feasibility of real-life contracts, through the benefits that they offer.
There are countless innovative global trends that have signaled changes in the way that companies create their business models and operate their businesses. Instant gratification is anticipated, hyper-personalized products are leading the way, companies and the individuals working with them are operating 24/7 in a more authentic manner, and machine-to-machine artificial communication is being widely adopted. These changes, coupled with a few other trends, are driving companies to rethink their business models and how they do business.
The new business models are based on:
- Real outcomes and tangible results
- Expanding into new markets and industries
- The shared economy
- Intricate, multi-layered networks
- Digital platforms (e.g. for marketing or CRM)
- Digitization of products and services
- Competing as a whole ecosystem
Innovations in the business models require new digital strategies, talent, people, and technologies. However you go about your digital journey, one thing is sure: digital transformation is unavoidable.