DATA AND ANALYTICS: Explore the Opportunities Everywhere!Drive Your Passion for a Fulfilling Career!!

Abstract:

The role of data and analytics is to equip businesses, their employees and leaders to make better decisions and  improve decision outcomes. This applies to all types of decisions, including macro, micro, real-time, cyclical, strategic, tactical and operational. At the same time, D&A can unearth new questions, as well as innovative solutions and opportunities that business leaders had not yet considered.

Progressive organizations use data in many ways and must often rely on data from outside their boundary of control for making smarter business decisions.

Digital Transformation 

Data and analytics is also a catalyst for digital transformation as it enables faster, more accurate and more relevant decisions in complex and fast-changing business contexts.

Both individuals and organizational teams make decisions, for example, when a person considers whether to buy a product or service, or when a business function determines how best to serve a client or citizen.

Data-driven decision making means using data to work out how to improve decision making processes. This leads to the idea of a decision model, which can include prescriptive analytical techniques that generate outputs that specify which actions to take. Other analytical models are  descriptive,  diagnostic  or predictive 

OPPORTUNITY EVERYWHERE, POWERED BY DATA AND ANALYTICS

The spur in the IoT technology and number of connected devices today ensures that a large volume of data is now available form fitness trackers, sensors in vehicles, home automation systems and mobile devices. Leveraging this data from these IoT devices allows insurers to create new product strategies, for example, using the information from customer’s smartphone devices using the GPS features, insurers can offer flexible travel insurance for the number of days abroad, rather than someone having to buy a fixed duration upfront.

Opportunities in the Sectors

In Trucking and transportation insurance, companies now have the opportunity to proactively reduce the claims that would follow an accident and/or pilferage, by monitoring the vehicles and the material.

Insurers can adjust premiums based on the provision of real-time driving patterns and metrics from connected cars as compared to static variables like age, gender and driving history.

Besides, IoT data has now enabled insurers to enhance the capability of their software to factor in user behavior and use this cognitive information to determine the price of risk.

The insurer can also inform the user what their actions mean so that an individual or company can act to mitigate changes in risk.

The following use case examples combine the predictive capabilities of forecasting and simulation with prescriptive capabilities:

• Forecasting the risk of infection during a surgical procedure combined with defined rules to drive actions that mitigate the risk

• Forecasting incoming orders for products combined with optimization to proactively respond to changing demand across the supply chain, without relying on historical data that might be incomplete or “dirty”

• Simulating the division of customers into microsegments based on risk combined with optimization to quickly assess multiple scenarios and determine the optimal response strategy for each

Guidelines for setting up a unified data strategy

The key steps in planning data and analytics strategy are to:

• Start with the mission and goals of the organization

• Determine the strategic impact of data & analytics on those goals

• Prioritize action steps to realize business goals using data & analytics objectives

• Build a data & analytics strategic roadmap

• Implement that roadmap (i.e., projects, programs and products) with a consistent and modern operating model

• Communicate data and analytics strategy and its impact and results to win support for execution
• Take Data Engineering Seriously – if done right, this can give a huge impetus to the data scientists
• Invest in building data engineering skills.
• Socialize across an organization, get stakeholders on board
• Create a cross organization data governance
• Invest in increasing data quality
• Champion standardization – and first time right
• Discourage data hacks

Data and Analytics governance?

Data and analytics governance — also called “information governance” — specifies decision rights and accountability to ensure appropriate behavior as organizations seek to value, create, store, access, analyze, consume, retain and dispose of their information assets. It’s critical to link data and analytics governance to the overall business strategy and anchor it to the data analytics assets that organizational stakeholders consider critical.

Data and analytics governance encompasses the people (such as executive policymakers, decision makers and business D&A stewards), processes (such as the D&A architecture and engineering process and decision-making processes) and technologies (such as master data management hubs) that provision trusted and reliable mission- critical data throughout an enterprise.

Notably, while governance originally focused only on regulatory compliance, it is now evolving and expanding to govern the least amount of data for the largest business impact — in other words, D&A governance has grown to accommodate offensive capabilities that add business value, as well as defense capabilities to protect the organization.

Effective data and analytics governance must also balance enterprisewide and business-area governance with a standardized enterprise approach. D&A governance does not exist in a vacuum; it must take its cues from the D&A strategy.

• Data centers physically house servers (as opposed to warehouses, which are data structures housed on servers or in the cloud). Their future depends on the degree to which workloads can be moved to the cloud. Those migration decisions must be based on the business benefits of doing so.
• Data warehouses provide an endpoint for collecting transactional, detailed (and sometimes other types of) data. They support predictable analyses for data whose value is well established — that is, well-known, predefined and repeatable analytics that are scalable across many users in the enterprise.
• Data lakes collect unrefined data (in its native form, with limited transformation and quality assurance and intrinsic governance) and allow users to explore and analyze it in a highly interactive way. Data lakes don’t replace data warehouses or other systems of record; rather, they complement them by storing unrefined data that may hold value. The sweet spot for data lakes is the world of pure discovery, data science and iterative innovation.

Data fabric

Data fabric is an emerging data management design that enables augmented data integration and sharing across heterogeneous data sources. Data fabrics have emerged as an increasingly popular design choice to simplify an organization’s data integration infrastructure and create a scalable data architecture.

If widely implemented, data fabrics could significantly eliminate manual data integration tasks and augment (and, in some cases, completely automate) data integration design and delivery. However, data fabrics are still an emergent design concept.  No single vendor currently delivers, in an integrated manner, all the mature components  needed to stitch together the data fabric. Ultimately, organizations must decide whether to develop their own data fabric using modernized capabilities spanning the above technologies and more, such as active metadata management.

Data fabric also consists of a mix of mature and less mature technology components, so organizations must carefully mix and match composable technology components as their use cases evolve.

Conclusions

The future of data and analytics therefore requires organizations to invest in composable, augmented data management and analytics architectures to support advanced analytics.