ADVANCED ANALYTICS & DATA SCIENCES

With the ongoing Digital Transformation initiatives across organizations, businesses need to walk the tightrope. They have to balance between being nimble to move towards 'Data Democratization' and, at the same time, they need to be absolutely cognizant and prepared for the risk of data security, privacy, and cyber-attacks more than ever before.

Datamatics has deep expertise and proven experience across both ends of this equation. It starts by following a robust Data and Information Security framework. The company understands the fact that Data Security concerns do get accentuated while working through multiple moving parts of critical datasets in large-scale Data Warehousing exercises. Datamatics ensures to build multi-tier secure systems in terms of access controls, and secure data networks, be it on Cloud or On-premise, and carefully documents all security implications across data movements, integration, and proposed architectures.

Datamatics has the ability to leverage Data Analytics to diagnose the causes of potential data breaches by processing and visualizing relevant diagnostic patterns. Data Analytics tools can provide system-wide visibility and solve issues like anomaly detection or building capabilities to contain threats before they spread. This allows organizations to save significant time and money by fortifying and building strong mitigation plans around data security, privacy, accuracy, and any potential threats.

Analytics adoption is a committed journey between key stakeholders and business leaders across the client and the vendors. It's not a single-stroke operational project that ends after building the committed models, dashboards and/or simulators. Rather it’s a journey that actually starts once we've built the first set of models and needs constant monitoring, tracking, tweaking and iterations to further 'Test' and solidify the 'Training' models built.

Unlike traditional research companies, Datamatics does not stop at recommendations. Datamatics provides complete back-end operations coverage from programming surveys to processing survey data leveraging Advance Analytics and support the clients with the implementation of our suggestions – be it Process, Technology, Integrated Business Intelligence Dashboards, or evolving Data Science models. Datamatics goes a step further and offers Process Audits, Consumer Insights and Consulting support to understand how well the suggested actions are implemented and highlights the inherent blind spots.

By systematically leveraging Data Analytics and building robust Connected Data ecosystems, organizations can start leaning on better, sharper, and faster data insights to cut down on operational costs and increase revenue. Consistent data management also helps minimize the redundant costs and efforts in dealing with inherent data complexities, inaccuracies and gaps at the very root levels. By monitoring the trends of data using Data Analytics, organizations can streamline the overall enterprise data management process by improving RoI and customer-centricity, enhancing CX, and optimizing spends across the board.

Customers’ needs are fast evolving, even in the most traditionally set, relationship-driven, and domain expertise-based industries. If customers don’t start seeing that gradual evolution of a data-driven relationship from their long-sustaining partners like you, chances are that they might start weighing such opportunities elsewhere.

In highly trust-based B2B relationships, it’s not just about selling more or retaining existing accounts. It’s more about right-selling, consulting on business potential, emerging trends, and threats, and being a strong data & insight partner for your clients. Many B2B companies lack the capabilities to translate data into relevant, usable insights that help them to sell more effectively by improving their understanding of their customers’ experiences, needs, and triggers. However, B2B companies can now acquire these capabilities to leverage data and analytics and add services that bring new elements of value to customers. In fact, these capabilities can also open up new sources of revenue in the form of enhanced product and service quality, CX consulting, risk identification and mitigation, or customer lifetime value-based segmentation.

Business should analyze their business requirements vis-à-vis the following pointers -

- Do you ever feel the lack of agility across complex organization-wide data and analytics initiatives that still do not offer enough consumer insights or data democratization for your front-line sales and marketing teams?
- With the perennial focus on 'Urgent' initiatives, do you ever feel the constant de-prioritization of multiple 'Important' data models that could've provided your teams the much-needed competitive edge?
- Are data-driven initiatives still remaining siloed at functional levels with not many forums for deliberating on a 'single source of truth' about your customers?

If the answer to all these questions is largely 'Yes' or 'Maybe', then leveraging an external or additional Data Science as a Service (DSaaS) partner can allow you to plug these essential gaps. It will allow you the flexibility to control the scale of these models as needed and to quickly create required test environments. It can also reduce capital expenses or licensing costs, and more importantly, save the critical bandwidth/capacity by keeping the infrastructure maintained and updated.

Using a DSaaS option can also provide access to the vendor's proprietary data science algorithms, data consulting expertise, thought leadership, and proven successes across industries and diverse business challenges. To add, when it comes to Datamatics, you also get access to our entire suite of Intelligent Automation proprietary products like TruBot (for robotic process automation), TruBI (for BI dashboards), TruCap (for automated data capture & digitalization) and TruAI (for AI/ML practices across text, voice, video and image pattern mining).

Many companies have invested in collecting and storing data from multiple sources. However, they do not realize the value of the data they are collecting. As they analyze each discrete data source across piecemeal projects, they miss out on valuable context and fail to build a holistic understanding of their customers.

The trick is to take a step back to comprehend the entire customer data ecosystem. How is it created? What is it used for? What are its applications? How accurate is the data? Is a single point in time value more essential than a trend value over time?

Datamatics’ Connected Data strategy assists businesses in assessing the maturity of their current data ecosystem based on the following key data principles:

• Volume: Develop a plan for data sets and their historical availability that would be considered for the Data Warehouse. Also the structural specifics of this Data Warehouse.
• Velocity: Speed is critical in day-to-day businesses for data accessibility. The team will research and organize the correct technologies to safeguard data and develop it as accurately and close to as current as possible.
• Variety: Identify all the unusual trends of data insight into an ecosystem and obtain the right tools to feed it back into the Data Warehouse.
• Veracity: Keep the data clean by avoiding the garbage-in-garbage-out approach and ensuring the data is precise and authentic.
• Value: Not all information gathered is of great importance. Hence we will build a big data environment that is highly actionable and easy to understand.

Using Big Data to understand consumers depends on the relationships between the data sources and the ability eventualizes when the data is connected. This Connected Data empowers you and your key business leaders to take proactive business decisions based on factual data, which was always there with you, but was not utilized to the best of its capacity!

Datamatics strongly believes that domain expertise is one of the most important skills, beyond the technical skills and knowledge stack required for effective Data Scientists. There is no doubt that in order for a Data Scientist to be able to support a specific specialist area, he/she needs a deep level of industry-sector understanding, business metrics, macro and micro levers and of course, a strong pulse of the leading and lagging indicators.

It’s hence that hiring and nurturing of domain-specific Data Scientists has always been Datamatics’ key focus area while scaling-up the team. The fact that Datamatics happens to offer services to a wide range of industries from BFSI, CPG/Retail and TMT to Travel, Education, Healthcare, and Manufacturing, allows the company to keep strengthening its expertise through a range of business cases and live projects across sectors.

All our Advanced Analytics and Data Science projects have Domain Experts as a part of the core team, who consult and guide the Data Scientists on specific business nuances, key drivers, and outcomes. This is a key differentiator that allows our team to get straight-off-the-mark during all kinds of business engagements.

Datamatics’ Analytics as a Service (AaaS) approach is based on the 5D model. It takes data curation and baselining as a key starting point for any engagement. It engages with relevant stakeholders in a structured manner across the Data to Intelligence to Impact journey. Datamatics provides Enterprise-class Analytics, sets up, and administers all the required elements – right from Data Warehousing, ELT, OLAP, to reporting and dashboarding.
The five stages in the Data Analytics journey are – 
1. Discover and Analyze: This is setting the data baseline in the form of Business blueprinting, Data Exploration, and Annotation. It’s the most critical phase comprising of an elaborate framework of Data Maturity Assessments and Recommendations. The team identifies data gaps in the current data ecosystem (for the desired outcome) and recommends initiatives to bridge the same. 

2. Design: This involves designing the most appropriate data architecture, process flows and fleshing out the prototypes.

3. Develop and Build: This is the crux of model building in the form of various iterations and fine-tuning, deciding the final data variables as well as building and testing the model.

4. Deploy and Implement: After the required phases of testing, the models built are tested, both from a statistical and User Interface (UI) perspective and testing the accuracy scores of the predicted output.

5. Deliver Value: Datamatics team monitors the progress of the model predictions and fine-tunes while working through iterations. It prescribes specific action points for the business and continues to provide enhancements and proactive support.

To enable businesses seamlessly integrate Analytics output in their existing workflows, Datamatics offers - 
•  Customized reports and dashboards for different business users delivered on a predefined schedule or triggered by certain events.
•  Dynamic reports and configurable dashboards with the possibility to drill down, pivot and filter data for deeper analysis.
•  A self-service analytics platform with secure role-based access.