Advanced Analytics & Data Sciences

Analytics is not a sporadic event but rather a continuous process. A well-designed implemented Analytics strategy can become a compelling competitive edge for strategic and tactical decisions across key business issues. When a company is equipped with the right-time decision-making capabilities, it improves its ability to provide the right tools to the right business leaders (at the right time and speed) to proactively respond to customers’ and business demands. In a fast-paced customer-centric business environment, the long-sustaining competitive edge can be established only through such data-driven strategies and agile decision-making that are faster than your competition.

As Customer Satisfaction is both a pre-requisite and a barometer for success, customer journey mapping is the perfect place to integrate next-gen technologies, such as Artificial Intelligence (AI) and Advanced Data Analytics. Data Analytics allows business leaders to eliminate the guesswork across the full spectrum of the Customer Experience (CX), thereby strengthening the bond between customer and brand. Even the simplest of data analysis (such as Pareto Analysis) can provide companies with insights that can help them enhance the overall CX by improving transactional Customer Satisfaction across each channel and touchpoint. Any incremental gains across Customer Satisfaction have the potential to bring high-impact exponential gains across CX and Brand Loyalty metrics. Data Analytics provides organizations with the ability to quantify the relative impact and priorities of each such customer initiative toward the essential business metrics.

Data Analytics is driven by building and embedding a strong data culture and is typically a long-term strategic decision. Eventually, in the long run, the right data and the correct analysis methods available will enable organizations to make high-impact forecasts cheaper and more scalable. A typical moderate-length strategic project takes an average of 10-12 weeks of deployment time with the required team of Data Scientists, Data Engineers, and Business Consultants. The key variables at play are your key business priorities, the immediate outcomes or low-hanging fruits and assessing the maturity of your existing data ecosystem through a structured Data Maturity Assessment (DMA) exercise. The implementation costs vary according to the degree of complexity of the data resources and the underlying data framework. The costs (and RoI) can be tracked to immediate result areas for the organization and then built up from there - in terms of costs, timelines and simultaneously strengthening the data ecosystem.

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!

Most organizations do have in-house Customer and Market Insights (CMI) teams. However, they still face unpredictable bursts for Analytics requirements in specific projects. Datamatics Advanced Analytics and Data Sciences team helps businesses address such requirements through various modes of flexible engagement models. These models are based on the nature & flow of the business workflow, vision, and business context. These are –

• Project Execution: Here, Datamatics engages at the Project level and undertakes the entire project with committed deliverables on a 'fixed cost' basis.

• Time & Material (T&M): The T&M contract comes into play for larger projects with less up-front certainty about the full scope of work. It involves diverse skill sets, for different time periods at different phases of the project. Skill sets include Data Engineers, Business Analysts, Data Scientists, Solution Architects, etc.

• Dedicated Teams: Businesses require ongoing support for model building, iterations, evolution and implementation. For such a continual kind of workflow, Datamatics recommends dedicated teams or Full-Time Equivalents (FTEs) stationed at the businesses’ work locations.

• Resource Augmentation: Datamatics identifies profiles and shares with the business for project/ dedicated requirements, with a minimum number of hours to be billed per month. The project management and deliverables stay with the business. The key advantage of this model is that the business does not have to invest fixed-costs in hiring niche resources for their teams.

• Extended Delivery Centers (EDCs): A dedicated team working from the Datamatics office, which is flexible to scale-up or down-size as per the business cycle. EDCs have become prevalent as an effective way to drive speed, scale, and flexibility within parent organizations. The EDC functions as an extended arm of the business and brings economies of scale by efficiencies across a range of projects and capabilities.

• Joint Venture: Datamatics teams work together on a solution/project wherein the business invests its vision and market experience and Datamatics invests Research and Technology expertise, thus maximizing each other's capabilities.

• Build, Operate, and Transfer (BOT): Datamatics team develops and deploys a product/solution/application custom-developed for specific business requirements, maintains, and improvises it during the entire deployment phase and then trains and transfers the model to be operated by the business teams. In fact, not just products and applications, Datamatics also engages in setting up Analytics CoEs or Practice Areas for businesses by deploying the BOT model from Inception to Scale. With growing requirements around data compliance and regulations, privacy and security controls, IP rights or the kind of PII or PHI data involved, many Datamatics customers do wish to develop and retain the Analytics capabilities in-house for higher operational control and risk mitigation. In such cases, Datamatics customers leverage its expertise in creating robust Connected Data ecosystems, building predictive & prescriptive models and institutionalizing high-impact BI dashboards. Datamatics builds such fully operational teams and data structures, and then ensures seamless transfer of such teams and knowledge base along with structured training, hand-holding, and governance models.

Datamatics partners with its customers to support them with a large pool of resources to suit not just their Analytics needs, but even the wider net of Insights & Process Consulting, Business Intelligence, Digital Experiences, and Automation, at scale and on demand.

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.