As organizations race towards digitization, it is imperative that the underlying infrastructure keeps pace. The need for ‘anywhere, everywhere and always-on’ IT infrastructure calls for a paradigm shift in this space.

The technology landscape is full of solutions that can parse historical data, but modern businesses increasingly want to predict the future. What are the risks? What are the opportunities? Where will the market – and revenue – be five years from now? Using predictive analytics, organizations can take steps toward answering these future-focused questions.

The 12 strategic technology trends for the coming year per Gartner shouldn’t surprise anyone; they focus on automation, security and AI. The technicalities go deeper, of course: hyper automation and autonomic systems, increased security through mesh and privacy-enhanced computation, generative AI, decision intelligence and AI engineering. But the larger issues are generally familiar. One trend is particularly familiar – the use of data fabric – because of its recurrence on Gartner’s list. Data fabric’s return is both notable and important; it underscores the need for a unified view across the democratized and ever-growing landscape of technology offerings in data storage and processing.

Driving efficiency and revenue by moving beyond the scope of simple chatbots

The questions on everyone’s mind

Financial institutions have been slow at adopting cloud technologies primarily due to concerns around security, regulatory compliance, and governance. As a result, they have been facing business model-related challenges like legacy technology, high running costs, and lack of scalability.

Open banking is an emerging financial technology where regulated financial institutions share some customer financial data to permit competition, innovation, and better financial products

Big data is driving the development of applications in today’s connected world. Organizations now need immediate support for instream processing of data using modern analytics platforms to develop use cases like fraud detection, health care services, and weather forecasts, among others.

Over the past decade, we have witnessed an unprecedented technological disruption in application and data functions. They are now enabling enterprise-wide digital transformation initiatives to empower customers and colleagues.

Indian agricultural sectorpresents some staggering numbers. It provideslivelihood to 58% of India’s population with aGross Value Addition of 265.51 billion USD(agriculture, forestry & fishing combined)1. At283.37 tons, India had a record production offood grains in 2018-191.

The UK government has mandated that energy suppliers install smart metering systems across Great Britain by the end of 2024. Regulators estimate that energy suppliers will deploy 53 million smart meters during this period

The total cloud computing market worldwide is expected to reach $623.3 billion by 2023. Much of this growth will go toward the public cloud. The reason is simple: it can address complex processes while offering global availability, agility, elasticity, flexibility, and a reduced total cost of ownership. At a basic level, public cloud computing offers compute, storage, and networking – and every data environment requires all of these to perform well.

First, take control of your data

Quick and effective decision-making capability is essential to respond to a crisis and recalibrate in order to explore, innovate and embrace new ways

As COVID-19 transforms the way consumers behave, it will affect demand estimation, customer targeting, and product and service fulfilment strategies.

There has been considerable improvement in use cases ranging from facial / voice / image recognition. One reason could be the abundance of labeled input data sets. We have as many photographs of human beings, cats, or dogs as we can. The more the machine can play and learn, the better it gets. The same as what happened with chess or Go.

The advancements in areas like machine learning, robotics and IoT devices will bring a drastic shift in the way claims are processed, managed and reported today, thereby resulting in better operational performance and improved customer satisfaction

A greenfield analytics journey begins with descriptive analytics to make sense of the data in place and understand current and historical performance better. Sounds simple enough, but setting the right stage in this phase is key to harnessing the right buy-in, sponsorship, and ultimately, driving adoption.

The rapid spread of COVID-19 poses a global threat in an increasingly interconnected world.

The strategy for optimizing pricing and maximizing profits

Building smart cities and smart healthcare with edge analytics and 5G

Defining migration strategy – Defining strategy for extraction of data from different source systems, cleansing of data, validation and reconciliation of data, environment planning is very tricky and needs a customized approach suiting the project requirement.

Over the last few years, growth of the telecom industry in North America has been quite static. The subscriber market has saturated. The movement of subscribers from one operator to another is driving the growth of cheaper service providers. Interestingly, the number of postpaid subscribers has increased as compared to prepaid subscribers, adding to the revenue potential.

Most large companies have IT Asset Management (ITAM) and IT Service Management (ITSM) systems in place. ITAM/ITSM systems manage assets, which include not only hardware, software, networks and services, but also employee and customer data.

Digital Voice User Interface will take enterprise human-machine interaction to the next level

IoT, 5G and AI are ushering in a new era of customer and citizen engagement.

The winning combination of human and artificial intelligence

Data privacy is an increasing concern among organizations.

Product Information Management solution delivers to organizations strategic advantages that greatly enhance the digital experience of both, its internal as well as external stakeholders

Cracking the code of multiple intents in single input with Machine Learning

Designing ethical & unbiased solutions through strategic governance

A recent industry survey identified that fraud and compliance monitoring is increasingly the focus of senior management1. This is due to the rising frequency and value of fines being issued for non-compliance. Firms typically spend 4% of their total revenue on compliance, but that could rise to 10% by 20222

Over the last decade Artificial Intelligence (AI) has been the most talked about technology and at the same time, most difficult to implement, given the broad spectrum of applications it embraces.

Every business needs to have a scalable, reliable model in place that addresses all business functionalities and enables understanding of business performance in real time.

A global consumer goods company was facing acute business challenges such as delayed product recommendations, deferred billing and SLA non-compliance despite 1000+ node big data cluster.

Fast decision-making is a key ask for the business to stay competitive in market. It is crucial that the business gains insights from its enterprise dataset and takes necessary and timely action.

DataOps helps overcome the hurdles and complexities and deliver analytics with speed and agility, without compromising on data quality

UDM is the driver of success for the organization that wishes to benefit from the enormous dataset (master and transactional) being hosted in any of its larger application landscape.

Product Information Management solution delivers to organizations strategic advantages that greatly enhance the digital experience of both, its internal as well as external stakeholders

New data analytics platforms are utilizing the processing power of source data systems for reporting and analytics, without having to store anything in the memory.

The foundation for high performance analytics

Data Lake is highly relevant as a single repository of data in original source or raw format for the entire enterprise.

ETL modernization with its cost-saving approaches to transactional and analytical data processing is becoming a key strategy for organizations’ IT estate rationalization

Having the right set of processes ensure consistency in delivering promised business outcomes through intelligent systems powered by the right technology and right capability

Mergers and acquisitionshave become quite the norm in the age of uncertainties as it opens up new opportunities like diversification, and control over new markets and technologies

The most important part of the decision making process is finding the right data, and organizations are making a huge investment in modernizing their data landscape.

The acute need to become an insights-driven enterprise

It is vital that the leadership understands the potential impact of AI across the value chain and drives the AI adoption initiative.

Algorithms and analytics allow us to learn and adapt in a continuous loop, helping us make exponential progress over time. They are the backbone of the intelligent enterprise.

Artificial Intelligence (AI) will be changing the way we look at manufacturing value chain.

In the media and entertainment industry, there has been an explosion of content, new channels of engagement such as augmented realty and virtual reality, and the inevitable shift to digital.

Over the years, asset-intensive industries like energy, utilities, and manufacturing have witnessed several service failures and safety incidents around machines and structures without warning.

The secret to AI success

The Six Sigma methodology is not new - it has been around since the 80s. What is interesting is its long shelf life. It continues to be relevant to modern day operations despite rapid technological advancements.

The rewards for using data are big. Smart organizations are using it to improve user experience, personalize interactions, improve loyalty and usage, create better products and processes, improve quality, lower waste, leverage preventive maintenance and reduce costs.

Today, AI to most businesses is a technology that aids in the automation of tasks and augments decision making by providing insights to key leaders and decision takers.

The retail industry, now, has access to a wealth of data across a customer’s lifecycle. The data helps predict customer behavior, assesses needs for new products, and triggers buying decisions.

Growing adoption of Artificial Intelligence (AI) in systems that assist, rate, and offer advice on how people are treated and what opportunities they are offered is resulting in broad discussions on how to build objective systems.

Amazon successfully used automation and artificial intelligence for order recommendations and fulfillment in their ecommerce platform and warehouses, but when it came to evaluating job candidates, the scoring engine failed

The Algorithmic Accountability Act has been introduced in the USA to specifically provide a legal reference framework to cover issues such as algorithmic biases that may affect delivery of services to citizens and consumers.

With the growing client expectation of faster time to market, most of workforce efforts in the IT industry are spent in maintaining the infrastructure of the project instead of focusing on the actual work items.

Cloud has been a great enabler in this race towards digital, building a connected cosystem that is essential to not only satiate the need of customers but also partners, vendors, employees and things.

In the digital age, digital data lakes have replaced file cabinets, and every organization is sitting on an astounding amount of data.

A data protection process encrypts the data and removes personally identifiable information from data sets, so that the actual people whom the data

The need to understand business performance in real time in order to manage the modern enterprise makes it important to overcome existing planning systems bottlenecks

Discover how to humanize AI to deliver analytics that matter?

Organizations are rapidly adopting advanced analytics to enable data-driven business decisions. As a result, the demand for data science experts is growing.

Financial crime detection and prevention is critical in any organization and how augementing it with Artificial Intelligence and Machine Learning will help industries

Artificial Intelligence (AI) is a topic that’s garnering a lot of interest in today’s digital world. Let’s understand the roles of database in the AI space, specifically analytical applications.

Real world evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of the data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources

CFO's roles have evolved from being official bean counters to that of strategic partners. CFO's responsibilities & experiences make them to most suited to embrace as well as advocate technology adoption

Insurance fraud detection is a challenging problem, given the variety of fraud patterns and relatively small ratio of known frauds in typical samples.

Harnessing advanced analytics and AI to optimize the value from IoT

Money Laundering is a threat of greater than expected magnitude. The risk associated with poor AML practices cannot be underestimated simply because crime methodologies are getting highly sophisticated and need equally intelligent counter measures.

The benefits of targeted marketing are two-fold: one, the total cost of marketing and acquisition decreases, and two, a well targeted campaign increases the likelihood amongst target audience to respond. This leads to enhanced response rates and Return on Marketing Investment (ROMI)

When the CFO-CRO relationship is strengthened, we can expect to reap the benefits of operational stability, predictability of returns, enhanced reporting, increased compliance and improved profitability.

With ever increasing volumes of data, relying on data processes without a governance layer is equivalent to sitting on a bomb waiting to explode.

Challenges in Risk Management can be addressed with technologies leveraging KRIs, as risk technologies will put Enterprise Risk Management more centrally in organizations

Going into the future, a thumb rule would be to bear in mind that investments in database appliances would do better if they had a wide availability of tools – hopefully from the open source space – and were built with industry speci¬fications and standards in mind

Key trends directing future investments in Enterprise Risk Management

Smart factories are the future. But they don’t have to mean abandoning your current investments in technology, data management and monitoring

Data Envelopment Analysis is a very powerful technique to evaluate the relative efficiency of business units when multiple inputs and outputs are involved.

As the digital wave sweeps the business world, traditional approaches towards managing data are impacted significantly such that methodologies and techniques require an overhaul to support this new digital age data

Rising need of a Unified Cloud Brokerage Governance Console - a services hub - to facilitate consumption of an increasing diversity of cloud services

Astute technology companies have relied upon online technical and non-technical communities to help other users with product or service features, highlight glitches, raise red ¬flags, sight early warnings, discuss performance issues, etc.

Data is the genesis for insights but the question is how value from data has indeed created protable avenues, just as it has from oil.

A road map for transformation and rationalization that entails an unbiased understanding of the BI Inventory stack, enterprises can unlock the benefits of a cost-effective, agile and intelligent BI platform

Machine Learning and Artificial intelligence helping Organizations enhancing data quality for better insights

Key differentiator would be an effective discovery environment that would enable rapid decision making and implementation of new offerings quickly in the longer run

Paper guides you in understanding the role of analytics in optimizing Smart Meter rollouts.

There is an increasing recognition that CFOs and CROs are not on opposite sides of the battlefield. Instead, it is more appropriate to think of them as two sides of the same coin. But, the question to ask is how can this realization actually add value to the organization?

It’s all about speed (but a controlled one), as to how quickly we can make the jump from raw data to actionable insights in the digital age. This calls for access to the right data at right speed at the right time, in fact at all times to ensure there is continuous supply of source data coming into your analytics platform (on-premise and/or cloud) to prevent your actionable insights from going stale.