Data-driven decision-making has become a realistic strategy for businesses today. Especially with the trend in cloud computing and data management strategy such as Data as a service (DaaS) on offer, more and more companies are turning to the solution for data integration, management, storage, and analytics. Rather than formulating strategies motivated by gut feelings and half-baked opinions, it is more reliable and sensible to use the available data to make business decisions.
Data mining is integral to business intelligence and helps generate valuable insights by identifying patterns in the data. For instance, analysis of historical information of a company helps to identify trends and aid in making decisions for the future based on successful and profitable practices, thereby reducing the occurrence of risky assessments.
There’s no doubt that data is a valuable tool for any business. A suitable example is online retail giant Amazon. Its focus on data-driven marketing strategy analyses billions of data points to test different things in order to find out what works and what does not. No wonder in 2020, the company reported net sales valued at $125.56 billion, a 43.6% increase compared to $87.44 billion in the same quarter in 2019.
Data encouraging business growth
Many businesses are already adopting data-driven decision-making (DDDM) to grow their business.
Decision-makers are using data to find out cost-effective ways of recruitment and product promotions, marketing campaigns optimization, fraud detection, customer loyalty improvement, leads generation, selection of publicity channels, etc.
Even non-commercial cases of data use, for example, in the transport sector are huge. Transport authorities can manage and develop their timetables, build new infrastructure, manage staff and control the flow of passengers using public transport. Experts are using big data to analyse accident trends on the roads and develop effective preventative solutions. Efficient traffic and crowd management models are already being implemented in major cities around the world. Geographic and spatial data mining that extracts geographic, environmental, and astronomical data to understand topology and distance is being extensively used in the travel, navigation, and governmental sectors.
New technology such as artificial intelligence (AI) will use data to code software that has the potential to change industries such as agriculture, health care, logistics, manufacturing, customer care to transportation through automation making them many times efficient than we know them today.
Over the years, the use of data science has gone beyond just being a tool for optimization, and has become an essential component to build new products and services.
However, to be a data-driven organization, strategic implementation of data management must be in place to achieve improved organizational consistency, increased productivity, greater collaboration and communication, faster and knowledgeable business decisions.
Role of ICT players
Creating intelligent systems that learn, adapt, and act autonomously rather than just execute pre-defined instructions offers many opportunities and challenges for the ICT sector.
ICT players would do well in considering the below areas for leveraging data science for their customers:
Skilled resources: Acquire highly skilled cross-functional experts who can provide support and development in the creative, user experience, analytics, technology aspect of businesses. Adoption of the best programs in data science, business analytics, and big data will be worth the consideration.
Agile software development: With the trend of virtualization on the rise, it is key to adopt a collaborative development approach coupled with design-thinking of software to deliver proofs of concept in order to maximize responsiveness to changing business needs.
Tried and tested solutions: Save data processing, development, and time by leveraging industry best practices, third-party data sets, accelerators, and tested algorithms that aim to meet customers' needs.
Mobile team: Real-time data processing systems such as bank ATMs, traffic control systems, and modern computer systems such as the PC and mobile devices require continuous data management. Thus, it is necessary to promote innovation and collaboration through trained teams who can be deployed at any location on demand.
PaaS offerings: The migration of applications towards the cloud will warrant Platform as a service (PaaS) that will enable the delivery of everything from simple cloud-based apps to advanced, cloud-enabled enterprise applications on a pay-as-you-go basis and accessibility over a secure internet connection, thereby fast-tracking the value of data science.
Collaborative ecosystem: Operators, vendors, and other ICT stakeholders must collaborate to support the growth of startups by adopting leading industry practices, based on the latest technology and business innovations.
By analysing data coming from the various application sources and historical data, it’s possible to get a clear understanding of the business activities almost anywhere and anytime. From using environmental sensors to create predictive models, to implementing recommendation algorithms to increase sales in the retail industry, effective data processing is critical for every project.
Big data has great potential to help produce new and insightful information, and there is a growing debate on how businesses, governments, and citizens can maximize the benefits of big data. ICT players lie at the heart of digital data storage, retrieval, and transmission with the potential to make business more efficient, effective, and responsive to customer needs. The ICT industry is a major development channel for business activities. It acts as a facilitator for the supply and allows access to a wide range of online services, increasing efficiency in institutions and corporations, with cost-effective promotion and enhancement of communication.
Having big data become a buzzword in today’s digitally driven era, the ICT industry will have a major role to play in harnessing data-driven decision-making to shape a better and simpler future for humanity.
