Why you should evaluate platform-driven Data Analytics?

Why you should evaluate platform-driven Data Analytics?

A few days back, we asked this question on LinkedIn.

What does having a great Data Analytics Platform mean?

  • Confidence in accessing and using the right data from a single source, without worrying about systems, formats, protocols, and security.
  • Flexibility to build your own custom big data and analytics applications, without worrying about tools and databases.
  • Ability to unlock new possibilities from your data, without worrying about the scale.
  • Capacity to extract, process, analyze and derive insights out of your data in real-time.

The consensus among our teams in TIBIL – having worked on several global client engagements across Data Engineering and Advanced Analytics – is that it means all of the above.

Before we get to a platform driven approach to Data Analytics, let us understand the business imperative for investment in Data and Analytics. Quite simply use of data analysis to drive competitive edge.

You may be looking to achieve it through deriving new insights to develop new products and solutions or designing and refining business strategies based on trends. Some other business requirements that can effectively be met through data analytics include:

  1.  keeping a tab on the pulse of the customer to make informed decisions to capitalize on a trend,
  2.  identification of a new market opportunities,
  3.  devising a new operational model.

Experience has shown that while business benefits are many, long term success depends on moving beyond the hype and embark on a journey to create the right platform that can help the business adapt, scale and innovate. A journey that delivers sustainable ROI.

While the actual benefits of an iterative analytics process usually come at a high cost a platform-driven approach to data analytics, can not only make the entire process cost-effective, but also improve productivity through faster iterations (a fail fast approach).

Fail fast. Finish strong.
Your ability to create value out of your data depends on your ability to identify the problem and create solutions based on the data – with agility. The faster you test your solutions in the market, the better to help evaluate the opportunity cost. This allows you the luxury to test different hypothesis with a faster feedback cycle, thereby improving the ability to roll out solutions faster.

A platform-driven approach helps you to move fast by leveraging reusable components, microservices and API based architecture, thereby allowing you to focus only on the tweaks to your solution or data models at a faster pace and lower cost. This finally leads to faster time to market of your final solution, at lower cost, allowing you to finish strong.

Talk to TIBIL Solutions. Our Data Analytics Platform offers enterprises a jump start on their data and analytics journey, with all the features an enterprise grade platform needs, as well as the flexibility and customization you require.

Data Gravity: Are you maximizing the opportunity?

Data Gravity: Are you maximizing the opportunity?

In the world of Data and Analytics, the term Data Gravity is now almost a decade old. The question is how well you are recognizing the opportunity and trying to maximize it.

For starters, Data Gravity refers to what happens when we move to a Data First philosophy – which anyway has become inevitable today. Data accumulates for the business every single second and it pulls your business to it – infrastructure for storage and management, people for analysis, applications for processing it and making sense of it. As data grows so does its density/mass and its influence on the business.

Increasingly, today, when we speak of Data Gravity, we are referring to the shifting of data to cloud and with it the applications and tools that are used to manage the data and analyze it. Most of the businesses worldwide generate and use as much external data as they generate internal data. In several cases the external data could be much more than internal data. And much of the external data resides in the cloud. For example, a company’s data from social channels is invariably being generated and stored in the cloud. Hence, many of the applications or solutions being built to effectively store, process and leverage that data are becoming cloud-based. After all the location of your analytics has a direct correlation to the time taken to move from raw data to insights.

Coming back to our central question. How do you maximize the opportunity presented by Data Gravity?

  • By creating a data storage , cleansing and enhancement system that gives you the ability to connect to it all  different data sources
  • By providing a secure, consistent and timely view of data, across both on-premise and cloud resources, to all the different users, including internal and external
  • By building the right analytical tools that reduce time to insight

When you are faced with a whole range of data sources, types and systems that are generating the data, coupled with so many different users of data with their own unique needs – this is easier said than done. Rather, this is where the crux of today’s Data and Analytics challenge lies. Can you navigate through the teething problems of data ingestion from multiple sources, processing of data of different types, its secure presentation to multiple users and its preparedness for supporting advanced analytics – easily, quickly? This means your teams can focus on what they need most – generating insights.

The answer to this lies in changing the lens on traditional data engineering and analytics. Adopt a platform driven approach to data – multiple sources are linked to this platform, multiple users are connected to the platform, multiple applications run on this platform. Sounds exciting? How about moving into this platform and having it tweaked to your unique needs rather than building one?

Check out what we at TIBIL are offering in this space. Ask for a demo.

Who says SMBs don’t need or cannot afford Data Analytics?

Who says SMBs don’t need or cannot afford Data Analytics?

A question often debated when it comes to Big Data is ‘whether small and medium businesses (SMBs) need to invest in Data Analytics? Moreover, does it give enough ROI?

Some commentators on technology believe that Big Data is for big enterprises; the analytics and visualization needs of an SMB can be achieved with easily available online tools. In addition, we see several cases of big data projects failing in several large enterprises. The heavy buzz around Data Analytics and Data Scientist professions also led some to argue that Big Data is indeed a big bubble and SMBs will better off by staying away.

Every human action and interaction generates some data – some structured, and a lot of it in unstructured form. Companies that are able to capture this data easily and effectively and use it to make intelligent decisions quickly are staying one step ahead of the competition. The puzzle is the complexity, volume and the speed at which data is being generated. The solution is in cutting through the maze without burning a hole in the pocket (not to speak of the impact of large-scale digital transformation of the organization). In this context, the question of whether SMBs need powerful Data Analytics begs an answer.

SMBs, being in the same global market as large enterprises, are exposed to the same potential of Big Data. They deal with customers, play in the same competitive market, are an active part of the social network, comprise a huge chunk of the economy, and have the same ambition to capture the market share (or should we say the customer’s mindshare).

Every business captures some level of data. Irrespective of its size, an organization has the best chance to succeed when it transforms data into insight for shaping its business strategy. So, how does Data Analytics help SMBs?

  • As SMBs increase the capability to collect transactional, social and customer data, they will need the ability to process and analyze that data so it becomes useful to the business. The organizations that can do this confidently gain competitive edge, customer satisfaction and financial performance.
  • The goal of using analytics is to understand how customers digitally interact with your business and determine means by which you can improve your business’ success through marketing. This includes establishing sales patterns, segmenting users and building data sets that reveal important details about customers’ buying habits. With the right information, you can build effective, targeted marketing campaigns
  • Data Analytics is about a shift from retrospective business intelligence to forward-facing actions. Every business tracks its sales and inventory, its revenue and profit performance. When you infuse analytics into that data, you crunch it into forward-looking recommendations for efficiencies, like resource allocation, demand prediction and effective marketing. Further, you can use data from diverse sources (including customer touch points) to tailor your products and services; and design your customer experience.

SMBs also have an advantage when it comes to adopting Data Analytics for their business. They can be very clear about what outcomes they need and be agile in implementing. Instead of hiring Data Scientists or over simplifying the role and scale of Data Analytics, SMBs can take a long view of what they can achieve with Data Analytics. Building on that, they can work with a solutions provider who can offer an ingenious data solution that can be customized to their needs, easily implemented, and scale with their growth. You can talk to Tibil Solutions for such a strategic adoption of Data Analytics.

We do like to hear your views and use cases on how SMBs should create their own custom paths for using Data Analytics for business success. Please chime in.

Changes in Risk Management for BFSI companies demands rapid action

Changes in Risk Management for BFSI companies demands rapid action

Chris Skinner, author of books like Digital Bank and ValueWeb, says – Now we’re seeing what I call ‘the complete open sourcing of financial services’ through apps, APIs and analytics. So the front office relationship is in an app. The middle office processing is through an API, and the back office is all about for analytics.

The sheer amount and pace of change in banking and financial services over the last decade has been mind numbing, and near nightmarish for risk managers.

Even as banking has become fast, easy and personalized; the tolerance for any errors and dishonest business practices has dramatically decreased (rightly so). While digital transformation has opened new business models for financial services companies, customers’ expectations of banking services have tremendously increased. Risk functions in banks now have to manage new types of risk, including models and cyber; besides managing compliance with ever evolving regulations. Additionally, they are expected to deal with these trends at a lower cost, because banks (like other services companies) expect to reduce their operating costs substantially when they adopt new technologies.

The good news. Data engineering and advanced analytics are enabling new products, services, and risk-management techniques – enabling risk managers make better choices about risks. The challenge, of course, is in finding the right solution that can scale with the organization, cover all the bases, integrate seamlessly with the bank’s enterprise systems, and does all of this in a cost-effective fashion.

Let’s take a look at some of the key trends in Technology in Banking.

Winning customers in the highly competitive, globalized banking and financial services industry is a battle that is increasingly being fought on the digital front. As digital technologies are rapidly changing life and work in every other sphere, customers expect intuitive experiences, access to services at any time on any device, customized propositions, and instant decisions – from their banking. This entails re-imagining the bank / financial services company from a customer-experience perspective and digitization. The risk function plays a critical role here collaborating with the business and technology functions across the entire transformation journey.

Automation in Compliance
Omni channel banking has thrown up a challenge – how to accurately validate the identity of persons applying for new accounts or performing transactions. Whatever channels are used, for a bank to approve new accounts or any transaction, it must draw data from multiple, disparate sources, analyze it and demonstrate the risks quickly, for informed decision-making. Digitizing the underwriting processes and increasing use of data analytics are visible trends of automation in compliance.

Even as regulation is becoming complex and noncompliance less tolerated, banks have to eliminate human interventions in risks dealing with customers and seamlessly connect right behaviors to products and services. Quite simply, automation in compliance is the best way to ensure accurate oversight (that can save millions).

Real-time decisions and service
Gone are the days of filling up laborious application forms and surviving long IVR-driven calls. Banks now have to offer real-time answers to customer requests with customized processes. As Risk managers seek to find ways to help banks assess risks and make decisions without human intervention, they have to contend with the use of more non-traditional data sources. For example, some banks have re-designed account opening with much of required data prepopulated from public sources to make the experience as simple, fast and short. However, establishing a secure and customer-friendly approach for identification and verification becomes yet another challenge for the Risk manager.

Big Data
Humongous amount of customer data is available and accessible to banks, including customer-payment and spending behavior, social-media presence, and online browsing activity, to aid in risk-intelligent decision-making. Companies have started using external, unstructured data not only for better credit-risk decisions, but also for portfolio monitoring and prediction of profitability.

Machine Learning powered Analytics
Machine learning identifies complex, nonlinear patterns in large data sets and springs insights that make more accurate risk models possible. These models learn with new information they acquire and improve the risk function’s ability to predict continuously. Several banks and financial services companies have started using machine learning, especially in credit rating, collections, and credit-card-fraud detection.

Use of advanced analytics is not just about Risk. It is about serving the customers with excellence too. To quote Chris Skinner again – This, to me, is the battleground when I’m talking about the digital revolution, the digital human, the digital bank: If you do not get cognitive, predictive, proactive, custom analytics that give the customer far more informed view about their financial affairs, you will not be the partner for that customer in their financial future.

Well, Banks and financial services companies themselves have such large technology functions in their enterprise today, that many of them can be called fintech companies. When they look for data engineering and advanced analytics expertise, they need a partner who understands the industry and the risk function, has the experience of delivering cutting-edge, comprehensive, cost-effective solutions, and the ability to cover all the bases discussed above. At TIBIL Solutions, we have done it and are continuously evolving our solutions. Ask for a demo.

Empirical decision making for business excellence

Empirical decision making for business excellence

In Sales, Marketing and Business Development – as well as business strategy in general – taking decisions based on data (hard facts as it has been referred to in an earlier era) is not something new. In a HBR story, Kristina McElheran and Erik Brynjolfsson opine that at their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights.

We create strategies and take decisions, especially in marketing, based on certain numbers, trends and assumptions based on those. There have been two sweeping changes in the last decade that have fundamentally altered the way we use data to make decisions. (1) The opportunities to collect and leverage data have changed dramatically with the advent of digital technologies (2) The very characteristics of data have changed even more dramatically – velocity, volume, variety, veracity – again thanks to digital technologies.

How do these two shifts affect decision-making? Statistics and technology are being combined to make sense of the huge amount of data at our disposal today to access the data, pinpoint observations, craft insights around them, and create actionable steps to enhance decision-making. Products and services are being shaped around our understanding of the data – not just in the way we target and reach the customers, but also the way we market our services. We are not talking about change here. We are talking about a paradigm shift.

The way to be smarter in this new journey is to go beyond the excitement of how the data is positively changing business. As the value of your data increases, it needs to be managed to ensure it is consistent, reliable and useful. When you are choosing a technology partner to help you effectively navigate the challenges of big data, and make data work for you – keep the following in mind:

  • You require data from all the internal and external sources, legacy and current, structured and unstructured – across its different types and forms; standardize it to make analytics-ready; make it easily and dynamically accessible for different users within your enterprise (from data analysts to marketing managers to sales staff on the field). This means moving away from traditional methods of extracting, loading and processing of data to more agile and scalable methods (NoSQL/NoETL) and a cloud-based data management solution without losing the data integrity.
  • You have to put context to the data you have managed to capture and profile to draw the right insight out of it. This requires capturing customer interactions with your brand event by event. No wonder then the marriage of artificial intelligence and data analysis is one of hottest data trends.
  • You will want to adjust your marketing or product or business strategies in real-time to take advantage of perceivable trends. This requires the entire value chain of data engineering and analytics to be agile, flexible, technology platform-agnostic, integrated with your enterprise technology eco-system, and responsive.
  • Staying ahead of the curve requires using predictive analytics to understand patterns in data and making business decisions based on pattern analysis. By intelligently leveraging artificial intelligence and machine learning, predictive analytics can become more reliable and robust.
  • You can understand the finer details of data by using Visual Analytics, which will make your decision-making faster.

Your search for the best data engineering and advanced analytics expert – who can enable you to understand Data, manage it, make it easy to work with it, and lead through it – will end at TIBIL Solutions.

Our cognitive, cloud-ready Data Lake solutions help you translate data into competitive edge. We enable organizations make intelligent decisions in real-time by integrating various data sources to create a data lake and developing the analytics layer leveraging ML/AI algorithms. Check out how we helped leading global organizations. Download our corporate profile. Schedule a demo.

Your Technology, Analytics and Marketing teams are not alone!

Your Technology, Analytics and Marketing teams are not alone!

Every other sector of the economy perceives data as the magic potion – a super value resource, which when used smartly will deliver that winning edge. Over the last decade, some of the key technology investments organizations made have been in the area of ‘Big Data.’ Even today, amidst all the excitement surrounding the opportunities big data holds, we can see teams across Development, Analytics, and Marketing are more involved in ‘grappling’ with the data rather than gleaning powerful insights from it. If your organization is among those, you should know that you are not alone; and more importantly know that you need to get out of that logjam fast.

In a 2017 survey by NewVantage Partners, 95 percent of the Fortune 1000 business leaders surveyed said that their firms had undertaken a big data project in the last five years. Less than 50 percent said that their big data initiatives had achieved measurable results!

Gartner Marketing Analytics Survey 2018 says that the average team size of marketing analytics grew from a couple of people a few years ago to 45 full-time employees (FTEs). Yet when asked which activities marketing analysts spend the majority of their time on, data wrangling topped the list along with data integration and formatting.

Big Data and Business Intelligence

Every enterprise needs a technology-oriented process for analyzing data and presenting actionable information to help their people, management, as well as customers make more informed business decisions. And for this they need to analyze large amount of data-sets (big data) containing different variety of data types in order to reveal unseen patterns, unknown relations, customer interests, and new marketing strategies.

What is actually important is to convert the data into information and extract the valuable insights from this information. The existing analytical techniques are not fully equipped to extract useful information in real time from the huge volume of data that comes from diverse sources in different forms. So much so that, quite often, beneath the desire to use the widest possible set of data to support decisions there is great anxiety about the veracity of that data.

We do know that big data analytics plays an important role in making businesses more effective, helping to achieve better customer engagement and satisfaction, as well as operational efficiencies. The key objective is to aid data scientists, analysts and various teams to make effective business decisions by analyzing the huge amount of transactional and other forms of data, which was not possible with conventional business intelligence tools.

The challenges that undermine your Big Data projects

Let us look at data storage and management. The most prevalent method of storage and management of data for decades had been relational database management system (RDBMS). However, RDBMS can be used effectively only for structured data; and it falls short when it comes to dealing with semi-structured or unstructured data. In addition, RDBMS cannot handle large amount of data as well as heterogeneous data.

The big challenge is in extracting the hidden valuable information from big data because the traditional database systems and data mining techniques are not scalable for big data. The existing systems need to have immense parallel processing architectures and distributed storage systems to cope up with the big data.

The other challenge is curation. For better business strategies, professionals need relevant, cleaned, accurate, and complete data (in short managed data) to perform analysis. Management of data includes tasks like cleaning, transforming, clarifying, dimension reduction, validation, etc.

Let’s talk storage. Since big data is in terabytes and existing storage capacity is usually limited, it is not easy for enterprises to pick and choose data that is of greater value and data that is not relevant or which optimal set of attributes can represent the whole dataset.

Then we have processing. Data comes from multiple sources with high velocity, which needs to be processed in real time.

Data loading is another issue. Enterprises need to get data from multiple heterogeneous data sources into a single data repository. Multiple data sources should be mapped to a unified structural framework, tools and infrastructure, which can support the size and speed of big data and transfer data real-time.

Finally, the need for interactiveness wherein multiple users with diverse needs have to mine the data they need and in the form they need.

It’s no dark street

At TIBIL, we solve the puzzle of sheer volume, veracity, velocity and variety of data through our own unique integrated approach – NoSQL, NoETL, Distributed Computing, and ML/AI. Our prescriptive, cloud-ready, cognitive, agile and expandable Data Lake solution – Dattaveni – helps you overcome the challenges and let Big Data deliver all the opportunities and benefits it promises.

What does an integrated, real-time data management solution look like? It has to seamlessly integrate with your enterprise systems. It should enable access to data from your internal systems (ERP, CRM etc.) and external data (like Social/ Weather) in real time. It has to draw insights from your legacy data. It should be the platform for your cognitive tasks. It should allow you to scale with new data sources for changing business needs. It should also be your business intelligence system with no additional load. That’s our Data Lake Solution – Dattaveni.

Want to know more. Give us a shout.