Why Composable Analytics a pre-requisite?
What is Composability and its impact on fluidity of data in an Enterprise?
How does it benefit Specific Industries?
Industry shift from big data to Small and Wide insightful data.
How does Companies benefit from Composability Analytics?
Composable Data Analytics at work
Few Show Stunner Data Analytics tools which would be a boon for NewGen Organisations –
Giant strides to future
Why Composable Analytics “the New Normal”? / pre-requisite?
The world after pandemic, introduced the jargon “New Normal -Living with Uncertainty” which the business enterprises have been experiencing it as “Normal” scenario since ages – an environment of uncertainty and ambiguity that requires them to be nimble, innovation and investment or re-investment in data and analytics strategy.
Businesses do understand, Data as an important asset for organisation and data analytics is more critical than ever. In an increasingly digital world, businesses recognise the need for more advanced and flexible analytics applications to cope with increasingly complex and unpredictable business needs which pushed the companies by migrating to the cloud to provide more computational and data agility. Many companies have adopted embedded analytics to bring analytics closer to business processes and improve decision making. But the downside is the cloud computing model is far from perfect, which is remediated by composable analytics to up the level of efficiency. New use cases for data to support machine learning, artificial intelligence (AI) and analytics are endless. As the rate of change continues, embracing flexibility, agility and adapting to new challenges is crucial.
Why Composability? Its impact on fluidity of data across the Enterprise?
The main objective of composability is to make the user experience seamless by having workflows jump between applications without requiring the user to manually shuffle between the applications making room for fluid environment. Composability removes the need to deploy and oversee separate workload-specific environments.
Composable analytics can help you develop a composable infrastructure around the data points generated by best-of-breed process automation and intelligence tools.
Businesses need more advanced and flexible analytics applications to cope with increasingly complex and unpredictable business needs. Microservices or cloud-based tools add agility and enable users to receive business value more quickly. With augmented analytics becoming more prevalent within solutions, it is becoming possible to package and embed more advanced analytics capabilities hand-picked from the analytics stack within the business process.
Composability Analytics adds more value to the already existing on-premises platforms with variety of modules for those enterprises that are already heavily invested in them, by simplifying and aggregating the most relevant workflows for the user. It makes for more painless and efficient operations by bringing all required workflows together into a single user interface in the form of impactful reports using charts, graphs and dashboards making it more visually appealing instead of lengthy tables and worldly explanations overwhelming the users and Stakeholders into a seamless experience.
Industry shift from big data to Small and Wide insightful data.
What is the hullabaloo over Small and wide data? Let’s get started with a familiar term “Big data”. Big data is a large, diverse sets of structured, semi – structured and unstructured data collected by the organisation. Key characteristics of big data, such as volume, frequency, and variety, lead to its use being limited to building bigger picture ideas. It’s great for visualizing a particular market trend or understanding the distribution pattern of its components. In other words, big data is an excellent way to figure out whether you are looking at a tree or a building.
In contrast small and wide data is better at picking out more specific information and distinct insights from individual data components and drawing valuable comparisons. Wide data allows the analyst to examine and combine a variety of small and large, unstructured, and structured data. In comparison, small data is focused on applying analytical techniques that look for useful information within small, individual sets of data. Wide data is all about tying together disparate data sources across a wide range of sources to come up with meaningful analysis.
A real-life example of usefulness of wide data for a huge Micro finance bank wanted to know the consumption pattern of their products/services to as to target potent customers. So how come this wide data produce useful insights for the Micro finance biggie? The solution unveils/drops in the form of data analytics teamed with ML that help convert the wide data to useful insights like study the persona, trend of onboarding customers and suggest products/services to potential customers.
How does Companies benefit from Composability Analytics?
A pharmaceutical company had numerous applications that people across the organisations use when they need to get answers from other datasets. But the users must log on to the separate portal for each application to extract the reports/ data. This is when Composability through data analytics helps to extract data from various sources and produces insightful analysis relevant to their specific function or line of business, which can be easily shared with stakeholders or other teams.
Composability also makes on-premises platforms more agile and cost-effective — for those enterprises that are already heavily invested in them. It makes for more painless and efficient operations by bringing all resources (storage, network, and computational) together into a single user interface. For systems that can only be accessed on-prem and have a variety of modules, composability is beneficial in that it simplifies and aggregates the most relevant workflows for the users into one seamless experience.
How does it benefit Specific Industries?
Composable data is useful in supply chain management, particularly in telematics systems. For instance, the technology can monitor the location of a vehicle 24/7. By implementing composable data systems in telematics processes, fleet managers can track drivers anytime and anywhere. This allows for seamless communications between employees and managers, optimized route selection, and delivery tracking. On the other hand, drivers can also leverage composable infrastructure to access fleet applications, such as trackers, predictive maintenance records, and other software that can aid in their trip.
Composable Data Analytics at work: CCM along with data analytics and ML
Today enterprises are moving from rule-based exception reporting to solutions powered by AI which provides actionable insights, in terms of dynamic dashboards, centralised monitoring, management reports like understanding of data, costs, ratios, insights, trends, behaviour, which will enable a behavioural enterprise-wide change and prevention of fraud internal and external party. AI powered CCM intakes simulation data to train a Machine Learning (ML) model to make real time predictions possible throughout the data in review. Using ML, CCM becomes an Intelligent CCM as it not only predicts but learns over the period with enormous variety of transaction the business produces by anticipating anomalies and identifying red flag transactions.
Few Show Stunner Data Analytics tools which would be a boon for NewGen Organisations
Giant strides to future
Composable data and infrastructures are all about managing data use to maximize productivity within the business. by choosing the best internal storage systems and optimizing the distribution of data, businesses can boost their performance. There's no "one-size-fits-all" solution when it comes to choosing a data analytic tool. However, when it comes to IT infrastructure solutions, composable data provides a strong case against its alternatives. Its capabilities in bolstering business processes while still allowing for open access to relevant resources are vital for businesses to stay agile and competitive in today's fast-paced landscape.
To simplify organisational decision making with composable data and analytics transform uncertainty into opportunity to drive innovation and manage change effectively.