Cloud computing is ushering in a new era of technology focused on mobility. Enterprises must be prepared to fail fast, learn quickly, and mine their way through the four pillars of the data management life cycle (Data Integration, Data Governance, Data Visualization & Data Science), if they hope to remain competitive in today’s advanced technology ecosystem and survive the inherent pandemic conditions.
COVID-19 has increased the acceleration of emerging technologies, and it is apparent that most enterprises are facing steep learning curves in their efforts to migrate to the cloud. However, by following the failing fast technique, a process employed by technology teams to deliver high-quality functionality by rapidly changing course when a new path doesn’t work, is one that helps to ease the risk and encourage development teams to forge ahead. Failing fast must be encouraged by enterprise executives and should be integrated in company culture. A culture where teams are penalized for taking risks results in slow growth and lower quality output.
So where should businesses focus as they begin to strategically think about migrating to the cloud? Here’s a list of technologies we recommend adopting to remain competitive under pandemic conditions and beyond.
With the introduction of cloud-based platforms businesses now have the agility necessary to keep up with a fail fast methodology. Enterprises use less resources on testing new platforms, as they pay only for what they need. The cloud is aiding businesses as they transition by reducing the costs and risk associated with traditional IT projects. Innovation is not easy, but it is made easier by utilizing the cloud as a resource to scale up and down as testing requires without a large upfront investment.
Business intelligence (BI) transforms data into actionable intelligence by leveraging software and services. BI tools that access and analyze data sets can present analytical findings to an organization, which help inform an enterprise’s strategic and tactical business decisions. Generally, business intelligence is classified in two categories – classic BI and modern BI. Classic BI is defined as IT professionals generating internal reports based on in-house transactional data. Modern BI is defined as using agile, intuitive systems used to analyze data more quickly.
Recently, the biggest topic in data warehousing has been centered around the best approach to handling unstructured data. With different kinds of data structures, enterprises are adopting a variety of systems to manage the influx of data, resulting in complex and cumbersome infrastructures.
Data Warehouses today are becoming more robust with simplified architecture and support. Several platforms help to make it easy for enterprises to migrate their data to the cloud as well as provide the ability to run analytics from anywhere. Combining structured and semi structured data into one warehouse that is cloud accessible makes this revolutionary. This system provides the end-user with a faster, easier to use and more flexible system than traditional warehouse offerings.
Enterprises have multiple options for adopting cloud based data warehousing systems. While most have the capability to store big data sets, there are distinct differences between the players. Some of the leading Cloud Data Warehouse players are Snowflake (Runs on any cloud like AWS, Azure & GCP) , Azure Synapse, AWS Redshift, Google Big Query.
Artificial intelligence (AI)
The drive toward artificial intelligence is fueled by increased processing power, the availability of smarter algorithms, the use of cloud computing, and standard Rest APIs. AI-powered business intelligence can process a broader and more diverse variety of data than before. Three factors are driving the acceleration of AI: the progression of machine learning algorithms and the development of deep learning and reinforcement learning techniques, expanded computing capacity to train larger and more complex models faster, and the large amounts of data that are being generated to train machine learning models.
Artificial intelligence allows data to reach its full potential while achieving incredible accuracy. When algorithms are self-learning, the data may become intellectual property. Data can then create a competitive advantage. The use of artificial intelligence can also augment existing abilities and improve the performance of existing analytic technologies.
Examples of how industries are deploying AI include:
- Banking. Banks use AI for fraud detection and credit and risk analysis. Market recommendations are provided by automated financial advisers.
- Government. Governments use sensor fusion in smart cities. Facial recognition is used by legal authorities and law enforcement agencies to prevent crime.
- Health and life sciences. Data processing from past case notes, biomedical imaging and health monitors are used to advance predictive diagnostics and improve response times in patient care.
- Manufacturing and energy. In manufacturing, AI is used for supply chain optimization, automated detection of defects during production and energy forecasting.
- Communications and retail. Retailers use AI to improve chatbot function, customize shopping experiences and personalize recommendations to customers.
AI-driven development is expected to trend in 2021. The tools that currently target data scientists, including AI infrastructure, frameworks, and platforms, will expand to the professional developer community, including AI platforms and services. Professional developers will use tools powered with AI-driven capabilities in order to automate tasks related to development of other AI-enhanced solutions. As a result, enterprises can expect development of AI tools to accelerate. AI tools are even evolving to being enhanced with business domain expertise and automating higher-level application development processing stacks.
Executives should look for opportunities to pilot advances that are being explored in the robotics and AI tech space (Hyperautomation). By gradually increasing adoption of new technologies, enterprises can prevent the duress associated with rapid technical and cultural changes within the organization. It is important to note that the use of robotics and AI will require a team with a new skill set, and that enterprises will need to find or develop people with these skills.
The introduction of the cloud and other new technologies represent a paradigm shift in enterprise technology. IT executives now have the ability to gain insight into new business threats and opportunities more easily and quickly than ever. While the shift from legacy systems to the cloud is not without challenges, it is important to note that the cloud offers vast opportunities for growth and success.
Meet the Author: Veera Budhi, Assistant Vice President of Cloud and Analytics Practice
Veera Budhi is an analytical and highly adaptable management professional with 22+ years of extensive experience enhancing business outcomes across enterprise. Skilled in aligning end-user needs with long-term resolutions to complex business challenges. Track record of success in strategizing the plan, architecting the solutions, leading development and implementation teams aimed at improving quality and efficiency at organizations. Advanced expertise in occupying leadership roles in all facets of Sales, Software development, project and product life-cycle management. Accomplished communicator skilled in building and strengthening relationships across functions to drive cohesive, strategic operations.
Saggezza is a proven technology and consulting partner that delivers personalized, high-value solutions to accelerate business growth.