Sriram Sitaraman

Sriram Sitaraman

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Why should enterprises go for a hybrid cloud strategy?

Posted by Sriram Sitaraman on Jul 24, 2019 11:23:00 AM

Enterprises migrating to the cloud are often faced with the dilemma of choosing between public or private cloud. The right way forward is to choose the one that best suits your organization's workloads, and that could be public, private or even a mix of both.


According to the RightScale State of the Cloud Report, 2019:

  • 58% of respondents stated that hybrid cloud is their preferred approach
  • 17% opted for multiple public clouds
  • Just 10% opted for a single public cloud provider.

So what exactly is a hybrid cloud?

It is the combination of both public and private cloud solutions, where the resources are typically orchestrated as an integrated infrastructure environment. This allows the movement of app and data workloads between private and public clouds in a flexible way as demands, needs, and costs change, giving businesses greater flexibility and more options for data deployment and use.

We take a look at why hybrid cloud is often considered the best strategy, and when should enterprises go for it.

Why go for a Hybrid Cloud strategy

A hybrid cloud strategy brings both public and private cloud environments together, giving enterprises the benefits of combining the best of both worlds:

Public Cloud
The presence of a public cloud environment ensures:

  • Cost-effectiveness and scalability, which are often a primary reason for deploying the cloud
  • Instant provisioning for compute and storage resources on demand, making it easy to handle rapid and seasonal growth
  • Delivery of AI powered services exclusively through the cloud

Private Cloud

A private cloud environment offers:

  • Quick delivery of information, using an on-premise server. Thus a disruption to internet connectivity does not bring business operations to a standstill
  • High levels of customization and compliance regulations.

The combined deployment of these approaches, with a hybrid cloud, provides extended benefit to enterprises in the form of: 


Hybrid cloud solution offers each business the flexibility to choose what pertains to their needs in any specific scenario.oSO depending on their requirements, businesses can deploy the best in-class hardware, software, or services for their use.

Additionally, since there is no vendor lock-in constraints, businesses don’t need to compromise on their IT solution quality. Further, the hybrid approach allows for a flexible policy-driven deployment, that enables the distribution of workloads across public and private infrastructure environments based on security, performance and cost requirements.


For scalability in services or accounts, public cloud solution is the best route. But this brings with it the inherent risks of exposing sensitive IT workloads across the inexpensive public cloud infrastructure.

The private cloud component on the other hand, provides the lowest latency and the best security for sensitive information.

A hybrid cloud strategy thus comes to the rescue, providing the scalability of public cloud, and security of the private cloud environment.


The use of a private cloud infrastructure provides dedicated resources to IT workloads, improving their security status. A hybrid approach thus addresses the needs of businesses who not only require the fast and flexible development options that cloud-based solutions provide, but also the security and control of keeping certain solutions on-premises.

Cost Savings

According to reports, 64% of enterprises report cost savings to be a major objective of the cloud program in 2019. However, respondents also state that 27% of their cloud spend is wasted.

A hybrid cloud approach can bring in cost savings as they occur in both public and private cloud setups.

The use of a private cloud allows you to stick to the pay-as-you-go model. Here, you can purchase hardware, networking infrastructure, and software licenses as needed, and layout costs for staff to maintain and manage the entire solution. Additionally, you can provide adequate security, and pay for power consumption to run and cool the system.

Public cloud deployment allows you to provide cost-effective IT resources in times of demand spikes, without incurring capital expenses and labor costs. IT professionals can help determine the best configuration, service provider, and location for each service, matching your resources and requirements to demand, while offering cost savings at the same time.

So with the hybrid cloud strategy, you can save on costs while running your workloads in a private cloud in an optimized manner, while you only utilize the public cloud for extra capacity during peak demand seasons.

Hybrid cloud enables you to choose the best IT infrastructure for your specific needs. And by combining the two cloud environments, you will be able to run your business more efficiently, while also delivering a variety of products and services to customers. This brings high reliability, better customer engagement, and ability for businesses to diversify their spend and skills by picking vendors as per their capabilities, and not just to avoid vendor lock-ins.

When to go for Hybrid Cloud

Despite the advantages that a hybrid cloud brings, it may not be the best choice for every business. For example, in smaller organizations with a strict IT budget, their interests will be best served by a purely public cloud approach. The public cloud would be more adequate for them when weighed against the upfront costs of private servers, maintenance, time investment, etc. In such cases, it doesn’t make sense to go for a hybrid cloud strategy.

Other limitations of the hybrid cloud include:

  • Hybrid clouds can create a larger attack surface and data traversing cloud networks, making it susceptible to security risks
  • Data shuffling back and forth between private and public aspects of hybrid cloud can create latency, hence latency sensitive applications should not go for hybrid cloud
  • There's some complexity involved in setting up and managing an efficient hybrid cloud as there is one right way to interconnect the public and private cloud environments
  • Applications that require high speed are not suitable for hybrid cloud

Thus between public, private and hybrid clouds, it is often a choice between what an organization needs, and what are their limitations. As for hybrid cloud, they are considered most suitable when:

  • Organizations serving multiple verticals face different IT security, regulatory and performance requirements
  • Businesses are willing to optimize their cloud investments without compromising on the value proposition of either public or private cloud technologies
  • Staff have a good understanding of IT workloads and their essential characteristics that can make the complex hybrid solution work
  • Securing their cloud solutions is a priority, and businesses are willing to deploy secure private networks

If your organization is willing to make such changes and optimal investments in cloud solutions, hybrid cloud is the right strategy for you. Its custom cloud solution designed by keeping in mind your organization’s history, current state and future provides a more practical approach to addressing your current needs, as well as adapting quickly to the changing demands of the business.

Hybrid cloud can function as a catalyst in bringing about a digital transformation in businesses, saving you from the “one size fits all” approach to cloud infrastructure. And such businesses which are willing to adapt quickly to the changing market demands, will definitely stay ahead of the curve.

Srijan is aiding global enterprises in their platform modernization process with highly nuanced cloud migration solutions. As an AWS Advanced Consulting Partner with certified teams, Srijan has extensive experience in working with AWS cloud solutions, along with building cloud-native applications.

Ready to explore a hybrid cloud strategy for your enterprise? Just drop us a line and our expert team will be in touch.

Topics: Cloud

How We Built an Intelligent Automation Solution for KYC Validation

Posted by Sriram Sitaraman on Feb 15, 2019 1:52:00 PM

Financial institutions sift through a huge volume of documents as a key part of their operational processes. More importantly, the need for regulatory compliance means there is very low tolerance for error in these tasks.

However, document verification and processing for KYC validation, insurance claims, customer onboarding etc. are time-consuming processes across enterprises. By recent estimates, 26 days is the average customer on-boarding time for financial institutions. Organizations are also spending a lot on these processes, as they retain large teams to do the work manually. And scaling up operations just means employing more people.

Is there a way around these challenges?

Intelligent Automation Solution

While Robotic Process Automation (RPA) has a mainstream role in automating many of the manual processes in the BFSI sector. But this particular task requires AI with advanced Machine Learning algorithms to understand the documents in context. This is Intelligent Automation solution - blending AI with automation, which can create solutions that can read the documents, understand the content in context, and find patterns in the data.  

At Srijan, we created a POC for an Intelligent Automation solution for (KYC) validation, that can automate a key portion of the process. The solution employs deep-learning algorithm to scan documents and images uploaded by end-users, and classify them into pre-programmed categories.

Here’s a look.



The solutions is designed using the following technologies:

  • Convoluted Neural Network (CNN) using Python and TensorFlow
  • OpenCV for Computer Vision
  • OCR and MRZ packages

How It Works

The solution uses a combination of deep-learning based image recognition and classification models as well as Optical Character Recognition (OCR).  It is capable of:

  • understanding given text or image material

  • acting upon it according to a pre-trained set of rules

Let’s say we are working with passports submitted during the KYC process. Here’s what the solution does:

  • Scanning - to extract personal details and passport expiry dates

    • “Read” the passport, extract different sections of the main page, using OCR to read certain sections

    • Computer Vision solutions leveraging OpenCV are used to read the machine-readable zones in the passport

    • Deep Learning algorithms leveraging Tensorflow framework and OpenCV extract the photograph from the passport, as well as identify any “Cancellation” or other stamps

  • Compare extracted information with information available in the database, to validate submitted proof document

  • Based on the above comparison and validation, the solution can classify the document submitted, in this case the passport, as verified, expired, cancelled, or a data mismatch.

  • Cases that cannot be categorized with appropriate degree of accuracy or confidence are marked for manual classification

  • In case of manual intervention, a workflow is created where the operations team can validate manually and classify them

  • The model learns from manual classification, and over time can spot patterns and closely mirror the manual results. This is accomplished by automated retraining of the model including the newer data and manual classification data

How This Helps

With the KYC validation solution, enterprises can automate repetitive manual processes, achieving:

  • Speed: Faster turnaround at most stages of manual processes, to solve scalability challenges and time-critical needs. For example: document verification in 1/10th of the time taken manually

  • Accuracy: Rule-based algorithms executed by software makes sure that there is near-zero margin of error in processes

  • Efficiency: Intelligent automation means tasks are done efficiently, compliant to standard processes, and with minimal need for manual intervention. For example: reduce manual efforts for KYC verification by 70%

  • Resource Management: As repetitive processes are automated, organizations have the freedom to utilize their human resources for more value-added tasks.

Automation just a segment of the KYC validation can bring in a host of significant benefits, as outlined above. But the solutions can be extended to other BFSI operations, or even other industry use cases to deliver similar gains:

  • Passport checks at airports

  • Processing insurance claim documents

  • Reconcile financial statements

  • Resolve credit card disputes

  • Any other manual & repetitive processes that require documents to be validated or reviewed

Have repetitive manual processes that you think can be automated? Looking to increase cost saving on operations without compromising quality and productivity?

Let’s start the conversation on how Srijan’s experts teams can help identify key opportunities to deploy intelligent automation for your business.

Topics: Machine Learning & AI, Data Engineering & Analytics

Edge computing for IoT : what, why, & how

Posted by Sriram Sitaraman on Mar 8, 2018 12:25:00 PM

Gartner estimates that there will be around 8.4 billion connected devices installed worldwide by the end of 2017, up 31% on 2016, with roughly 37% of these devices set to be used by businesses and the rest by consumers. By 2020, it’s estimated that there will be more than 20 billion connected devices. Given this scale of IoT adoption, edge computing capabilities will have a significant impact on businesses, and their ability to compete in a dynamic market.

What is Edge Computing?

Edge computing refers to the processing of data of the Internet of Things (IoT) closer to where it is created, unlike cloud computing where it is sent over longer routes to data centers. In this way, data is processed at the edge of the network, by performing analytics and knowledge generation at or near the source of data. 

Examples of edge computing include a wide range of technologies, from wireless sensor networks, mobile data acquisition, mobile signature analysis to cooperated distributed peer-to-peer analysis ad-hoc networking.

What drives the need for it?

Cloud computing provides many benefits that today’s agile businesses can’t ignore, typically by transmitting data to a centralized computing location in the cloud. Cloud computing has made great strides in real-time data access, but latency is still an issue, which suggests universal centralization isn’t always the best idea for an organization.

IoT networks produce a huge amount of data. And even if it is possible to process this data, doing it on the cloud becomes impractical in terms of:

  • Cost-effectiveness

  • Computational capacity requirements

  • Relevancy

  • Network latency for critical actions

How will Edge computing power the future of IoT?

Irrespective of the industry you are in—whether it’s manufacturing, energy, transportation or any other—IoT will have a big impact on your business. Edge computing helps you manage and analyze all of the generated data at an increased speed with reduced load on the internet networks transmitting huge amounts of data. 

Edge computing use cases

Cameras, sensors, production line machines, cars and industrial equipment are a few examples of industries where edge computing is expected to play a larger role. Effective applications, enabling computation of data output at the edge would not only help in real-time decision making, mitigating any latency, but also save costs and improve RoI. A few use cases in various industry verticals are listed below: 

  • Oil & Gas: Edge computing is being deployed in a top-notch oil and gas company to detect faults at the machinery level, before they are found using predictive analysis.

  • Chemical Industries: It is being used to build smart petroleum refineries where the process is well analyzed to increase productivity and workplace safety.

  • Energy Sector: It is being used smartly in various energy producing industries to reduce power loss and make energy equipment reliable and efficient. 

  • Commuting Sector: Various transportation companies are making use of edge powered IoT devices and computing services to help find the right parking area and reduce parking downtime. Various AI algorithms work with these edge devices to optimize parking spaces and to collect real-time traffic and navigation data. They use analytics to make well-informed decisions.

  • Industrial Uses: Leveraging information at the edge, operations like shutting down systems can be carried out without having to query servers.

How it works

Edge computing works by pushing data, applications and computing power away from the centralized network to its extremes, enabling fragments of information to lie scattered across distributed networks of the server. Its target users remain any internet client using commercial internet application services. Earlier available to large-scale organizations, it’s now available to small and medium organizations because of the cost reductions in large-scale implementations.

Technologies used to enable computing at the edge of the networks

  • Mobile Edge Computing: Mobile edge computing or multi-access edge computing is a network architecture that enables the placement of computational and storage resources within the radio access network (RAN) to improve network efficiency and the delivery of content to end users. 

  • Fog Computing: This is a term used to describe a decentralized computing infrastructure which both extends cloud computing to the edge of a network while also placing data, compute, storage and applications in the most logical and efficient place between the cloud and the origin of the data. This is sometimes known as being placed “out in the fog.” 

  • Cloudlets: These are mobility-enhanced, small-scale cloud data centers located at the edge of a network and represent the second tier in a three-tier hierarchy: Mobile or smart device, Cloudlet and Cloud. The purpose of cloudlets is to improve resource intensive and interactive mobile applications by providing more capable computing resources with lower latency to mobile devices within a close geographical proximity. 

  • Micro Data Centers: These are smaller, reach-level systems that provide all the essential components of a traditional data center. It is estimated that micro data centers will be most beneficial to SMEs that don’t have their own data centers as larger corporations will tend to have more resources and thus not need such solutions.


For large enterprises with global operations, IoT, and consequently edge computing, are more a matter of 'when', rather than 'if'. The earlier businesses stat evaluating their requirements, the faster they can zero in on the necessary technology and implementation strategies. 

Srijan is already helping enterprises access and analyze their IoT data on interactive visualization dashboards. So how about we get that conversation started, and see how Srijan's expert teams can help with implementing IoT ecosystems and edge computing for your enterprise.

Topics: Machine Learning & AI, Data Engineering & Analytics

Automated Conversational Interfaces: The dawn of a new era in Artificial Intelligence

Posted by Sriram Sitaraman on Mar 1, 2018 4:38:00 PM

Artificial Intelligence (AI) is now rapidly becoming business as usual. Chatbots and voicebots are examples of artificial intelligence that are seeing a great uptake in consumer-facing organizations. The wave that started with intelligent personal assistants on smartphones was soon bein leverage to create smart home appliances that are getting increasingly affordable. And now brands are fast getting on the bandwagon to create friendly bots that greet and guide customers through online experiences.



Chatbots can fetch complex data—such as calculating the number of cleaning machines deployed at a site or tracking the machine count workable in a particular year—and report without human error. Powered by a combination of machine learning and natural language processing, chatbots can provide customer service and other commercial support functions, helping businesses gain an edge over their competitors. Most importantly, businesses can pick and choose the level of maturity of their chatbots, depending upon the type of tasks they expect it to perform.


Why Chatbots/Voicebots?

If you want your customers to be able to access information in a matter of few seconds without having to call any number and that too while surfing the net, the answer is chatbots (and voicebots for speech recognition). 

These bots are intelligent personal assistants which can answer almost any user question in the most user-friendly way and in the shortest time possible. They work based on a pre-recorded set of answers, and answer in the same generic tone without making your customers wait. They are built so that it is difficult to distinguish whether it’s a real person answering the customer or a technology bot integrated with a web application.

Your customers would never have to type

With voice bots, unnecessary typing would soon be a memory of the distant past. Through speech recognition, your customers will have the privilege of asking a query without needing to type it. 

When executed via voice commands, actions like editing images with the help of photo-editing software, booking follow-up meetings, setting up reminders, sending summary notes, and sharing files with the help of messaging apps would not be tedious tasks anymore. Voice has always been our most human, and most natural interface – so let’s make the most of it. 

24*7 personal assistants for your customers

Voicebots and chatbots are intended to add value to customers’ personal as well as professional lives. They are programmed to be useful “on-demand” assistants to make conversations with humans more productive. 

Chatbots can function like a personal assistant—without your customers having to hire new staff and train them or find additional space at the workplace. They can be built to schedule/cancel meetings and send/receive emails on behalf of customers. They can also send important reminders, giving you daily alerts on what’s scheduled for the day.

Automate business processes

Chatbots could be programmed to answer thousands of questions frequently asked by customers all day, thus reducing the burden of calls hitting the call centers. They can serve to help customers in various languages, without the need to hire people trained in those languages. They can also assist in keeping track of what consumers think about your business by means of sending feedback forms to customers, and also through conversations they have with them. To a great extent, they can single-handedly help businesses monetize their social media platforms and push business promotions and offers.

How Chatbots and Voicebots Work 

how bots wrk

Conversational interfaces work on the basis of carefully designed inner logic that allows them to respond from a wide variety of options in any given context. Here's a look at the working of a typical conversational interface:

  • A user on a messaging platform sends a message to the bot that is processed through Natural Language Processing (NLP)

  • Then, the bot can launch an action or answer with real-time information from a database/API, or hand over to a human

  • The bot improves with every message it receives; this is called machine learning. In case a human helps the bot learn, it’s called supervised learning

Given their large-scale adoption, and customers' increasing comfort in interacting with chatbots, it's time for businesses to start putting chatbots/voicebots in place to streamline customer interactions.

Srijan’s technical teams work on creating conversational interfaces which can handle every query that a user might possibly have. We start with understanding enterprise requirements and create PoCs to help stakeholders get to the desired prototype. Our teams then move to final production, aimed at solving specific business challenges.

Where can conversational solutions fit into your brand communication strategy? Chat with our AI experts, and explore how Srijan can help implement chatbots/voicebots for your enterprise.

Topics: Machine Learning & AI, MarTech

How Edge Computing for IoT is set to impact Businesses

Posted by Sriram Sitaraman on Feb 14, 2018 4:29:00 PM

Connectivity has rapidly evolved from connected people to vast networks of connected things (IoT) including industrial machines, a variety of equipment, controllers, and sensors. With data scientists treading to harvest benefits, machine data are typically transmitted to a centralized computing location in the cloud.

The volume of data produced in IoT networks is massive. For example - a Boeing 787 generates 40 TB per hour of flight whereas a large retail store with IoT infrastructure gathers 10 GB per hour. Sending massive volumes of data to the cloud, even if technically doable, is both cost-prohibitive as well as impractical due to computational capacity requirements, relevancy, and network latency for critical actions.

This is where edge computing for IoT comes into play. The concept of edge computing refers to creating computing capacities nearer to the source/ consumer. The most familiar use of edge computing is- locally processing data that’s relevant near source or consumer to reduce the traffic to the central data-center, and mitigate latency where real-time response is required.


How Edge Computing will impact businesses

The speed and agility benefits of edge computing are so great that in coming years IoT-created data will be stored and acted upon close to the edge of the network rather than in their centralized data centers. It would not bring surprises if equipment manufacturers started building edge-computing capabilities into devices and sensors themselves over the next few years.

A research firm IDC estimates that “by 2019, 40% of IoT data will be stored, processed, analyzed, and acted upon close to or at the edge of the network.” Here are some ways edge computing will impact businesses: ·

  • Real-time response times: Faster or real-time response is a necessity in most applications of machine learning that involve time-critical action points such as artificial intelligence, robotic process automation, connected cars, M2M communications, etc. The inability to respond in real time could mean loss of business, functional and safety issues. With the ability to process data at the source or nearer to the source, edge computing enables to act immediately. For example – a driverless car with the ability to process sensor data within the car to apply brakes, accelerate or maintain a certain speed, a production line machine turning itself off due to parts failure. ·

  • Dependable operations: Most of the data is processed at the source or nearer to the source, that does not need internet connectivity at all times. Edge computing enables equipment and other smart devices to operate normally without disruption even when they’re offline or Internet connectivity is intermittent. This makes it an ideal computing model for businesses that count on the ability to quickly analyze data in remote locations such as ships, airplanes, and rural areas—for instance, detecting equipment failures even when it’s not connected to the cloud.

  • Cost savings: Data that has local relevance is processed at the edge of the network, reducing the amount of data that is sent to the cloud. Reduction of traffic to the data center in the cloud saves data transaction costs (back and forth between sources and cloud) as well as the need for higher computing capacities at a centralized cloud.

  • Secure and compliant: Edge computing helps to address the security and compliance requirements that have prevented some industries from using the cloud. With edge computing, companies can filter out sensitive personally identifiable information and process it locally, sending the non-sensitive information to the cloud for further processing.

  • Interoperability between new and legacy devices: Edge computing converts the communication protocols used by legacy devices into a language that modern smart devices and the cloud can understand, making it easier to connect legacy equipment with modern IoT platforms. As a result, businesses can get started with IoT without investing in expensive new equipment—and immediately capture advanced insights across their operations.


Cloud Computing and Edge Computing

It’s expected that by 2020, up to 65% enterprises will use IoT. This means more complex networks of connected things including multitudes of machines, equipment, and devices that will produce a mindboggling amount of data. Most of the elements in these networks will require a faster processing and real-time response that centralized processing in the cloud simply can’t fulfill.

Edge computing will be able to deliver quick processing requirements and mitigate latency, intermittent connectivity and computational challenges in processing a vast amount of data. Cloud computing is going to stay but it will act more like an off-site location to draw patterns, forecasts, and reporting while handing over immediate processing to edge computing.

In the current landscape of industrial IoT and the pace it’s growing – it’s imminent that edge computing is going to take a lead pushing cloud computing to the second place, in near future.

Edge Computing - A complement to the cloud

Most industry experts are calling edge computing a successor of cloud computing that will have a symbiotic relationship with each other. We foresee the emergence of a strong relationship between cloud and edge computing, in which each will handle data for different computing tasks and data types while complementing each other. While edge computing will serve time-sensitive data for immediate intelligence within or closer to the device itself, the cloud will handle data intended for historical analysis. The next wave of the business transformation will be the Edge computing. The time to prepare for the future starts now, to avoid the risk of getting left behind.

Take a closer look at how Edge computing is being implemented, and specific use cases across industries.

Looking to evaluate your IoT and edge computing requirements? Write to us and let's explore how our expert teams can help.

Topics: Data Engineering & Analytics, Enterprises

Data Visualization Dashboards - A must have for lean enterprises

Posted by Sriram Sitaraman on Feb 14, 2018 12:53:00 PM

When your data assets are accessed by decision-makers in your organization, are they presentable enough to showcase critical insights and drive informed actions? The traditional tools fail to see the high-level picture and are unable to deal with the overwhelming volume of data, and are hence cannot support effective decision making.

Data visualization dashboards provide effective ways to make your data understandable and easy to manipulate in an aesthetical format. It helps enterprises realize rich and interactive data visualizations so as to take business decisions when it matters most.

Why do we need data visualization dashboards?

Data visualization dashboards populate visually stunning patterns for instant clarity into trends, helps draw patterns on multiple parameters, analyze them and derive prescriptive and predictive intelligence. Dashboards using tailored algorithms significantly facilitate streamlining of internal or external processes, and business decisions using real-time data.

Visualizing meaningful patterns through varied sources

Enterprises today, gather an unprecedented amount of data, the source of which could be various business processes, productivity tools, project management tools and various other sources.

Now, the need arises wherein every department and level of your enterprise should be able to make well-informed decisions. To enable this, the data coming from various sources has to be transformed into one common format, and correlated relevantly to make sense for decision makers. A few use cases are as follows:

  • Data coming from multiple sources such as business applications, production management tools, operation management applications, CRM, ERP, etc. presented on a common dashboard showcasing correlations using multiple scenarios
  • View, monitor and analyze key business metrics with interactive charts, graphs and patterns helps in deriving trends, anomalies correlations etc. without any technical assistance
  • Prescriptive actions availing historical data, and past performance across business functions Deriving predictive intelligence using deep learning enabled by past patterns and real-time data
  • Identify statistically significant trends and patterns to enable comprehensive analysis and effective decision making

Using the right tools

A quick scatter plot, time series forecast, and cluster analysis can unfold interesting stories that lead analysts to further questions and statistical testing. While developing a visualization dashboard, it is important to have right set of tools and algorithms that fit best for your business needs.

Enterprises are going for tailored data visualization dashboards that uses custom algorithms built over modern tools such as:

  • Tableau
  • Highcharts
  • PowerBI
  • D3.JS
  • Qlikview
  • Spotfire

It’s essential for large-scale companies and enterprises to have business intelligence dashboards that not only help with insights but also help optimize resources for higher profitability. Perhaps you also need a tailored dashboard if your organization relates to any one of the following:

  • An enterprise producing massive amounts of data coming from sensors, machines, people and processes across functions
  • A large-scale organization using multiple business applications - decision makers have to look at more than one place for insights
  • A growing organization consistently taking efforts to optimize resources for higher profitability
  • An organization not being able to leverage data assets to take quick informed actions at right time
  • A lean organization seeking efficiency and higher ROI

Srijan helped several Fortune 500 companies and large enterprises have transparency, derive intelligence and convert them into action points through tailored data visualization dashboards. Data Science and Analytics offerings from Srijan cover end-to-end services including data integration, master data management, warehousing, data visualization with tailored dashboards, advanced and predictive analytics for performance management. Let's get the conversation started on how Srijan's data science experts can help.

Topics: Data Engineering & Analytics, MarTech


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