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Create Customer-Centric Responsive Products with Big Data Analytics Solutions
Read on this article to learn what place data applications have within the software development industry.
Content Summary
Relevance of big data
Data-driven, not technology-driven
An agile approach to data-driven development
Conclusion
The software industry is responsible for some of the greatest advancements in the digital age, but to thrive businesses must have a single focus—servicing customer needs through responsive and adaptive products. The ability for businesses to do so is directly dependent on how well customer insights are incorporated into the design and development processes. Growth in the variety, volume, and sources of big data affords software development teams the opportunity to leverage insights to build products more aligned to the customer’s evolving needs.
Irrespective of the industry, customers expect businesses to rapidly modify products based on evolving needs and desires and cater to consumers’ more mobile lifestyle. By analyzing customer data, development teams can understand behavioral and usage patterns to design products that meet customer demands. The findings can provide software develoment teams with a more granular picture of the end user. By leveraging data insights, software development teams can discover hidden correlations between data points to design experiences the customer may not have known or expected.
To achieve this, software development teams must stay constantly abreast of the latest technologies, coding languages, and systems to better serve customers evolving needs and desires. According to a 2019 report by Statista, the usage of big data analytics in software development projects will grow at a 12.6 percent CAGR, reaching $46B by 2027. Evans Data Corporation predicts 28.7M developers worldwide will use big data and advanced analytics in development projects by 2024 to create more informed and customer-centric products.
Relevance of big data
The collection and analysis of volumes of holistic data—customer preferences, geo-location information, and more—from myriad sources, is intellectual gold for businesses. The ultimate goal of capturing and ingesting data from individuals is to extract actionable insights, allowing end users to enjoy a more personalized customer experience, and for businesses to drive revenue and brand loyalty.
For example, an app development company can analyze data points from a customer’s app usage patterns, purchase history, browsing activity, location, and spend. Armed with this insight, the company can suggest apps most relevant to a customer’s lifestyle. In addition, the company can create value through competitive offers tailored to the customer. Such data-driven insights are the key to building a relationship as a trusted advisor to customers by delivering a high-quality experience and personalized recommendations. Customers deriving greater value from the partnership are likely to rely on the business, increasing brand loyalty.
Data-driven, not technology-driven
While software development teams are by nature technology-driven, more focus must be made to become data-driven using customer insights. When combined with an empathetic, design-thinking approach, development teams can create and deliver personalized products and experiences.
When initially implementing data insights into the development process, data science experts should be involved to train to staff about using and interpreting data from advanced technologies, including artificial intelligence (AI) and machine learning (ML)— both of which are crucial for data analytics to scale. ML automates analytical model building of data and is a branch of AI, based on the idea that systems can learn from data, identify patterns and behaviors, and make decisions with minimal human intervention. To get the most value from AI and ML, data experts pair the best algorithms with the correct software tools and processes. Depending on the size of the business, data experts should work in collaboration with the CTO or CTO organization to align data analytics strategy with business strategy. Data experts must work with the technology department to design a methodology for incorporating data insights into product design decisions across the development process.
For example, during the early stages of the development process, software companies will carry out due diligence and capture customer personas and related useful data in order to personalize products. Initially, the chief technology officer (CTO) may act as a product owner and create a clear plan with a minimum viable product (MVP) in the software development process. However, as the development teams grow, the role of the CTO will evolve, giving way to the inclusion of a head of data or a chief data officer (CDO) to scale the infrastructure and capabilities of the development process. It is critical real-time data is incorporated into the process, not only to create new products but to iterate existing ones. The development process should be a collaborative effort to ensure products consistently adapt to customer needs.
Therefore, as big data analytics provides businesses with an opportunity to become truly data-driven, the CTO organization should work in collaboration with data experts for the benefit of the end product and provision of a highly relevant, personalized customer experience. During the development process, teams must build a valid and strong case to convince a CTO to consider a data-driven approach. This must be achieved by working in an agile way rather than enforcing the initiative from topdown.
An agile approach to data-driven development
Developers need not spend several months to develop and test a new app only to discover that it does not meet customer needs or expectations—successful oftware development revolves around facilitating an agile innovation approach. This ‘fail fast’ approach can be extended to big data analytics.
Open source distributed processing frameworks manage processing and storage for big data applications—Hadoop, the world’s most popular big data processing software has a CAGR of 53.7 percent to 2022. These frameworks offer time and cost savings by making data readily available for development teams to use at critical times during the development process, including prototype and MVP creation and iteration, customer acceptance testing, and more. The frameworks also help collaboration between different stakeholders (CTOs, CIOs, data developers, and data scientists) to become more transparent when capturing and utilizing the data for development. Information is easily accessible to all parties in case any adjustments are required during the development stage.
Many software development systems that utilize big data applications perform best when highly iterative, involving incremental development approaches. This means development tasks to create highly scalable big data applications are dealt with in an adaptive way, allowing delivery of releases at the end of each small increment. Data experts can use AI and ML technologies to analyze data, allowing for necessary modifications to be made automatically, efficiently, and cost-effectively.
Conclusion
Software development teams must work in collaboration with data experts to utilize advanced analytics when servicing customer needs to create responsive and adaptive products.
Source: Source: SoftServe
