Data-driven decision making (DDM) is a broad term that involves several related approaches.
It can be thought of as the application of statistical methods to improve decision making. Some of the approaches are statistical decision analysis, meta-analysis, neural network approaches or decision logic programming just to name a few. The more specialized and general approaches are important but beyond the scope of this article. Data-driven decision making is useful in any competitive industry and can yield substantial results when properly used or implemented.
What does it take to be a data-driven decision maker?
Data-driven decision-making (DDDM) is characterized as making decisions based on concrete facts rather than intuition, observation, or conjecture. As a result, facts, measures, and data will be used to impact strategic business decisions that align with your goals, objectives, and projects. Fundamentally, data-driven decision-making means reaching essential business objectives rather than winging it by depending on verified, analysed facts. Everyone in a company, whether a business analyst, sales manager, or human resource professional, is empowered to make better data-driven decisions on a regular basis when they grasp the value of their data. This on the other hand, cannot be accomplished simply by selecting the appropriate analytical instrument to find the next strategic opportunity.
Understanding what data is and the value it possesses may be one of the first questions to explore when considering using the data-driven decision-making approach. Every business, large or small, must leverage data in some way or another if it wants to succeed. Data sources include customer records, financial data, supplier information, regulatory data, human resources data, and many more. A good strategy aids businesses in asking the correct questions to achieve the greatest results.
Importance of data-driven decision making.
The amount of data gathered has never been bigger, but it has also never been more complicated. This complicates data management and analysis for businesses. According to NewVantage Partners, 98.6% of executives say their company strives to have a data-driven culture, but just 32.4 percent say they have achieved it. According to a 2018 IDC report, businesses have spent trillions of dollars to update their operations, but 70% of these programs fail because technological investments were prioritized over developing a data culture to support them. To become data-driven, many companies are building three key characteristics: data competence, analytics agility, and a community. It is not easy to change the way your company makes decisions but incorporating data and analytics into the process can yield the most substantial results. Financial services, telecommunications, healthcare, travel and hospitality, manufacturing or retail as well as platform businesses are all industries that leverage data-driven decision-making.
What are platform businesses?
There is a new wave of business model taking off: The platform businesses. These businesses are built on the internet and have not yet been sold or run on any real-world lines. In other words, a platform is a business strategy that creates value by allowing two or more interdependent parties, typically customers and producers, to exchange information. These interactions could be short-term transactions, such as connecting buyers and sellers, or they could be long-term social relationships (collaborations) to achieve a common goal, or ongoing efforts to help participants improve their performance by providing a support system that would help them learn faster together. To enable these exchanges, platforms harness and construct huge, scalable networks of people and resources that can be accessed on demand. The platform business’s goal is to provide a governance framework as well as a set of standards and protocols that enable large-scale interactions so that network effects can be realized.
Platform businesses have been around for years but are only becoming more popular as word spreads about their success and how simple they are to start and operate. In 2016, platform firms accounted for four of the top five members of Forbes’ list of most valuable brands, as well as eleven of the top twenty. In early 2017, the top five companies in terms of market capitalization were all on virtual platforms. Practically all of today’s most successful businesses, as well as most today’s largest IPOs and acquisitions, are platforms. Examples of platforms include Amazon, eBay, Twitch, Slack, Waze and Uber.
The four categories of platform businesses
Platforms are divided into four categories:
- Platforms for innovation: where developers or businesses can sell related goods and services. Consider the services provided by Microsoft, Oracle, and Salesforce.
- Transaction platforms: Amazon, Airbnb, and Uber are examples of transaction platforms that help individuals and institutions find one other.
- Integration platforms: such as Salesforce, are a technology, product, or service that serves as both – a transaction and innovation platform.
- Investment platforms: these have built a platform portfolio strategy and serve as a holding company. Some examples include Priceland and OpenTable.
But irrespective of the platform type, they all have four characteristics in common: They connect workers or sellers directly to customers, they allow people to work when they want, sellers are reimbursed for a specific activity or commodity at a time and payment is conducted through the platform.
Why are platform businesses great role models to learn from?
There is no denying it. Disrupting the industries, they are stifling long-term, profitable company models! simply because data-driven judgments underpin many of its operational decisions. Google, Amazon, and Facebook, for example, are three platform behemoths that rely heavily on big data. It is crucial to emphasize, however, that data-driven decision-making is not limited to platform business models: while their success can inspire others, data-driven decision-making can also benefit other industries. Here are a few reasons why platform business concepts are worth studying, especially when it comes to data-driven decision making.
Data exploration: The abundance of data and its exploitation by platform firms to retain a maintain a high level of artificial intelligence (AI) is perhaps the most crucial element supporting platform business models. Uber, for example, is increasingly forecasting and even organizing demand using algorithms that equalize the supply of available cars, rather than just matching rides to travellers. But how exactly does this happen?
Uber uses real-time data (number of ride requests, number of drivers available, weather, game) to allow its operations teams to make informed decisions such as surge pricing, calculating maximum dispatch ETAs, and forecasting demand/supply for its services, all of which improve user experiences on the Uber platform. Uber’s services combine real-time processing with streaming data to give actionable insights on a minute-by-minute basis. Uber’s real-time data processing, storage, and querying platform, Gairos, makes large-scale data exploration simple and efficient. This enables teams to better analyse and improve the Uber Marketplace’s productivity by utilizing data intelligence. An example of a use case is surge pricing. Gairos was re-architected for enhanced scalability, stability, and long-term viability, ensuring that it can continue to optimize its performance across an ever-growing set of use cases.
Another fantastic example is Amazon. When confronted with a vast number of options, customers can become overwhelmed. They lose sight of what would be the ideal purchase for them, and this is where Amazon comes into the rescue. To address the problem, Amazon programmers devised systems that evaluate large amounts of data collected from customers when they browse and use it to improve the company’s recommendation engine. The idea is to collect a large amount of data from customers to construct a “360-degree view” of each customer. Amazon’s vast, collaborative filtering engine (CFE) keeps track of client information. This data is then used to build a personalized recommendation system.
Last but not the least, let us look at how LinkedIn explores data. Have you ever wondered how LinkedIn remembers your job preferences, suggested connections, and favourite stories? LinkedIn’s success mantra is Hadoop and Big Data Analytics, which allows it to predict the type of information you will need and when you will need it. LinkedIn’s recommendation engine makes use of data to provide a variety of data products. A detailed image of a member and her connections is built using data from user profiles and other network activities. As you search for your dream job, LinkedIn understands who you should connect with, where you should apply for jobs, and how your abilities compare to those of your peers.
Better scalability: In contrast to platform models, companies with a linear structure whether it is a car manufacturer like GM or a subscription content provider like HBO, they own their inventory, which is shown in their financial accounts. As a result, such businesses are forced to operate on a decentralized structure from the start, and their ability to scale is heavily reliant on the availability of data they can base decisions on. Platforms, on the other hand, can facilitate the exchange of value produced by decentralized networks of individuals thanks to linked technology. As a result, today’s platform businesses can arrange exchanges on a massive scale, generating value through scalable, complementary types of connections, growing marketplaces, ecosystems, and communities. https://www.shujaazinc.com/ is a perfect illustration of this, as it is an African social network focused on connecting African entrepreneurs with formal business prospects. In fact there is no need to increase sign-ups or engagement on the platform because, rather than using a market survey to conduct research, it prefers to do A|B testing with real users to analyse how activities have resulted in value creation for the platform’s users.
Potential for innovation and growth: Here the emphasis is on how data-driven decision makers on a smaller scale could evolve into superpower business ventures in the future. Because it can easily handle many of its challenges, Delivery Hero a global pioneer in smart delivery solutions and a member of the DAX 30, makes over 1.3 billion deliveries worldwide. Order prioritizing and driver allocation, for example, are decentralized and based on data-driven rules. Furthermore, business innovations that otherwise could take years for corporate organizations to implement are easily piloted across multiple countries in a matter of days to gather evidence for a worldwide roll-out.
Significant number of applications: Finally, Platform business models offer a variety of adaptable use cases, particularly for sales and marketing decision-makers. Are you a sales and marketing professional or a company looking to make data-driven decisions? please take a few minutes to review our checklist to discover what lessons decision-makers can apply in their own businesses. To gain a better understanding of the impact AI is creating on the marketing industry, see our article on AI in digital marketing and sales on our pillar page.
Are you curious about the hype surrounding data-driven decision-making? Are you pondering whether it is a worthwhile investment? Or are you simply unsure where and how to begin the procedure for your business? We have got exciting news for you: As a starting point, platform business models provide a varied collection of use cases to pick from!