24 Mar 2022
3 Data Trends Set to Shape 2022
In God we trust, all others bring data.
– W. Edwards Deming
In an age when poor data quality costs the U.S. economy over $3 trillion USD per year, data has never been more valuable. With its dynamic growth and exponential influence on day-to-day operations, it’s no wonder managers across all sectors want to keep their finger on the pulse of the data industry. While there are many exciting developments to keep an eye on, three areas will dramatically impact 2022 and the post-COVID recovery: hybrid cloud use, collaborative data science and integration skills. Read on to learn why.
The rise in hybrid cloud usage will continue
Cloud solutions are becoming indispensable to organizations, delivery numerous benefits related to scalability, flexibility and speed – all while maintaining strict security and providing resilient dependability. But the development of cloud solutions should be regarded as in its early stages, rather than as a finished product. According to Gartner, “Growing geopolitical regulatory fragmentation, protectionism and industry compliance are driving the creation of new regional and vertical cloud ecosystems and data services. Companies in the financial and public sectors are looking to reduce critical lock-in and single points of failure with their cloud providers outside of their country.”
Overcoming vendor lock-in should be a priority for companies, since being forced to stay with an inferior product of service simply because of financial pressures, inadequate number of staff or out of fear of causing an interruption to operations can have cataclysmic results on a business.
Of course, making use of cloud platforms created in other countries means addressing other issues as well, whether they be connected to logistics, regulations or legislation. To this end, business representatives from different sectors are collaborating with IT experts, scientists and politicians to establish cross-border policies that make cloud solutions accessible and secure. One such example is Gais-X, a project which aims to create “…an open, transparent and secure digital ecosystem, where data and services can be made available, collated and shared in an environment of trust.”
Statista reports that hybrid cloud usage will increase from a 2020 level of $52 billion USD to $145 billion USD by 2026.
Implementing a hybrid cloud strategy simply means combining a private cloud with one or more public cloud services, which makes it easier for organizations to handle increasing amounts of data through transferring data according to needs, costs and goals. Deploying a hybrid cloud system empowers organizations by giving them more control over their data management, while maximizing resources in a cost-effective manner that does not compromise on security.
By linking multiple clouds, companies consolidate their resources and ensure that connections between various infrastructures, devices and networks are synchronized. Moreover, better organization and control of data goes hand-in-hand with increased flexibility, commodity organizations appreciate even more given the impact of COVID-19 on daily operations and long-term ambitions.
Collaborative data science
A collaborative environment for data science will help organizations gain a richer understanding of business challenges, problems and opportunities. This will not only lead to more projects being launched into production but will increase the quality of the work and calibre of the data professionals on staff, as a dynamic, supportive workplace will always attract the most talent.
Operationalizing data means sharing data across all departments within a business and eliminating data silos. If an organization suffers from data silos, the results can be disastrous. These include cumbersome decision-making processes that are not reflective of real-time, up-to-date data, decreased levels of trust and cooperation between teams, higher costs as a result of ineffective processes, technology and infrastructures, poor quality of data and uninspired employee experience and lover end-user satisfaction.
This is why forward-thinking organizations that want to drive growth, not just maintain status quos, are devoting time, resources and money to establishing, or strengthening, a collaborative data science approach. This means not just breaking down silos and encouraging cross-organizational data sharing, but also leveraging analytics and business intelligence (BI) tools to obtain the most value out of the data they collect.
While it could be argued that analytics and BI tools can work independently of each other, the reality is that choosing BI tools that fit your business and augment your strategies will enable advanced analytical capabilities. Broadly speaking, BI instruments transform raw data into manageable, digestible and actionable insights. As such, BI tools collect and collocate data from numerous sources and over various channels. The growth in BI tools is staggering. Fortune Business Insights estimates that from a 2021 level of $24.05 billion USD, the BI market will reach $43.03 billion USD by 2023, at a CAGR rate of 8.7% between 2021-2028.
Once data is gathered, either in a data lake (unstructured) or warehouse (structured), data scientists can employ data modelling techniques and analytics – descriptive, exploratory, predictive or statistical to evaluate data, extrapolate data for the purposes of identifying trends and make predictions. This process is made efficient and convenient with BI tools that provide data visualizations (graphs, charts, maps) and intuitive graphical user interfaces (GUIs). Having a detailed picture of data enables organizations to adjust strategies, anticipate market fluctuations and troubleshoot issues before they turn into problems.
Integration skills will drive data initiatives
The dynamic nature of modern IT, and in particular the exponential growth in data technologies, places even greater importance of continuous learning and development on the part of data scientists and engineers. While soft skills such as creativity, persuasion, collaboration, adaptability and emotional intelligence (EI) are valuable, there are certain hard skills which have become crucial in the field of data. One of the most important, according to LinkedIn, is cloud computing. Microsoft succinctly explains that cloud computing is “…the delivery of computing services – including servers, storage, databases, networking, software, analytics, and intelligence – over the Internet (‘the cloud’) to offer faster innovation, flexible resources, and economies of scale.
Cloud computing delivers enhanced ability to scale, eliminates the costs that accompany older hardware and software, increases the speed of processing massive amounts of data and provides businesses with unprecedented levels of flexibility. But the elastic nature of cloud computing should does not compromise on the innate reliability it also provides, as backing up data, maintaining business continuity and supporting disaster recovery are easier, and more cost-effective, to achieve. Finding IT specialists proficient in cloud computing is vital for companies that want solutions that scale to their growth and deliver the computing capabilities – both in terms of data storage and bandwidth – that they need for effective software delivery cycles.
Along with finding talents that possess cloud computing skillsets, companies should also look for people with experience with Kubernetes. Red Hat, the world’s enterprise open-source leader, released a report last year which indicates that 85% of IT leaders agree that Kubernetes is ‘extremely important,’ ‘very important’ or important to cloud-native application strategies. The reason why is easy to understand. According to Red Hat’s Gordon Haff, “Kubernetes, or k8s, is an open-source platform that automates Linux container operations. It eliminates many of the manual processes involved in deploying and scaling containerized applications.”
A 2021 survey from the Cloud Native Computing Foundation found that 96% of respondents reported using or evaluating Kubernetes. The same research reveals that 69% of respondents reported using Kubernetes in production and puts the number of Kubernetes developers worldwide at 3.9 million. A 2021 Pure Storage study showed beyond the aforementioned benefits of Kubernetes vis-a-vis containers, 55% of IT professionals believe that the increased operational efficiency Kubernetes achieves will reduce annual costs by 20% or more.
Data isn’t a response to changing market forces, but a catalyst
Given the aftershocks caused by ground-breaking new technology and the upheaval triggered by global events, sound business decisions can be the difference between record-breaking profits and bankruptcy-declaring emails. Gartner puts it nicely, “Effective decision making in today’s complex and disrupted business environments must be connected, contextual and continuous to drive good outcomes.” Focusing on data triggers development in other areas, like automation, artificial intelligence (AI) and cloud technology, to name a few. Data is, quite simply, the most valuable resource an organization can have – but the key is to leverage data to maximize operational efficiency, accomplish growth plans, generate new revenue streams and deliver rewarding employee and customer experiences. That’s why leading organizations to Software Mind, whose agile software development teams and data scientists deliver tailor-made solutions that are intuitive, evolutive and disruptive. Fill out the form and get in touch – our specialists are waiting to hear how they can help you achieve your business goals.