Managed Data Analytics Services

We engage our core technical team to derive insights from structured data. We help build the data analytics infrastructure and apply advanced data analysis to provide our customers with periodic and ad hoc reports, alerts, and timely predictions, in addition to, self-service analytics. As a result, the companies that make use of our managed analytics services can prioritize their business activities, plan their ongoing business management, change management, and the optimization of their business processes with high efficiency.

Czario’s Credentials in Data Analytics include:

  With more than a decade of strong data analytics experience, we have built competencies that cover traditional and big data, data management, business intelligence (including self-service BI and data visualization), advanced data analytics, and data science that are core to businesses today.

  Our Partnership with Microsoft, Amazon and Oracle and the Gold competencies we hold acknowledge the commendable expertise of our data analytics team

  Our substantial domain expertise and our work for niche industries like manufacturing, retail and wholesale, professional services, healthcare, financial services, and telecommunications have put us on a high pedestal with regard to Data Analytics.

  We have been rated A+ by BBB and recognized as one of the Fastest Growing Data Analytics Companies.

Complete Data Analytics Outsourcing to Czario

  Master data and metadata management, data quality assurance and data security schedules

  Periodic reporting, including the ones leveraged with advanced analytics

  Access to self-service analytics

  Ad hoc reporting with the specified complexity structure and level

  Alerting of different priorities, for instance, alerts requiring immediate attention

  Fine-tuning of machine learning models, if applicable to the solution

  Additional analytics activities and services are provided based on the time-and-materials (T&M) model mentioned in the SLA

Partial Data Analytics Outsourcing

If you have a centralized enterprise analytical solution, but, don’t have time or resources to leverage its usability based on specific analytical needs, we suggest outsourcing the untouchedscope of workto our core data analytics team. In this case, we offer you the complete integration of our data analytics infrastructure with your central data warehouse and closely collaborate with your support team in delivering results with our expertise and specifying improvement plans.

Czario’s Managed Analytics Service Process

Discovery

This is the first stage where we analyze your requirements and as-is situation by scrutinizing the existing data and data quality practices, and the existing analytical solution. We examine the company’s business plan and collate departmental inputs in order to better structure the customer’s analytical needs and map it to solutions. Based on the findings of the discovery stage, we finalize the service and the service level agreement (SLA) terms with the customer.

Transition

We extract crude analytical data into a data warehouse, enhance and shape it to ensure that the data is of high quality and relevance, integrate with the customer’s data warehouse, create OLAP cubes for exploratory analysis and train machine learning models. In this stage, responsibility transfer takes place.Responsibility we take is high: we deliver value though analytics having high freedom in process and resources:

  We set up processes and manage them for you efficiently with the substantial aspects covered

  We are responsible for allocating and managing the core resources required for implementation.

Service delivery

We provide access to self-service analytics tools, regular reporting tools, and ad hoc analytical mechanismson receiving the customer’s request. We then set up alerts for business users that notify detected anomalies or threshold overruns. We can also deliver accurate forecasts that will become the basis for optimizing a company’s internal processes in a future-proof way.

Continuous improvement

Our agile approach helps us adjust to the customer’s changing business needs to provide relevant reporting. We help in adding data sources, both internal and external to derive performance-based metrics that help in the decision-making process. Our constant endeavor is to work towards increasing the quality of data analysis including the accuracy of predictions, tweaking the machine learning models based on the increase in the volume of historical data, and more.

The Types of Data that we Analyze

Traditional data

  Financial data

  Transaction data (e.g., sales, orders, payments, returns)

  Customer, vendor, partner data

  Asset data

  Product data

  Process data, and more

Big data

  Customer data

  Employee data

  Behavioral data

  Sensor data

  Web data

  Geospatial data

Unstructured data

  Text

  Images

  Video

The Various Methods that we apply to Analytics

  Data aggregation (traditional reporting, online analytical processing)

  Forecasting (based on machine learning, including deep learning)

  Clustering

  Classification, e.g., defining a transaction either as fraudulent or normal or the machinery condition either as pre-failure or functioning normally

  Optimization, e.g. inventory optimization

  Statistical hypothesis verification

We Promote a Transparent KPI-based Collaboration

We take KPIs that cover important measurements of data analysis such as quality, ad hoc analytics, user satisfaction and security to build a collaboration bridge. The KPI set may look as follows:

  The availability of the service to business users and its ease of use

  Timeliness of the report updates

  Alert system or mechanism precision (alerting on significant events)

  The number of ad hoc reports compiled and categorized by complexity

  User satisfaction score for each user type like analytics consumers, explorers and analysts

  Vulnerability assessment scores

Business Areas that we support

Customer analytics helps in customer profiling, understanding customer preferences, evaluating their response to promotions, and preferences for price and product combinations.

Marketing analytics

Marketing analytics helps to measure the success and the downfalls of marketing activities, helps mark market trends, benchmark competitors and industry norms, and analyze a product portfolio to evaluate improvements.

Sales analytics

Sales analytics helps to check accounts by type and geography, the latest opportunities and successful campaigns, product performance and more.

Ecommerce analytics

Ecommerce analytics helps to conduct a complete customer analysis: segmentation, engagement with products, basket analysis, customer journey analysis, forecast customer demand, analyze the performance of categories, brands and SKUs, and implement the recommendation functionality.

Performance analytics

Performance analytics helps to conduct plan/actual analysis, monitor strategic, departmental and individual metrics and always be up-to-date with the progress achieved.

Financial analytics

Financial analytics helps to handle specific financial tasks like effective cash flow and working capital management, and contribute to establishing a true partnership between decision makers.

HR analytics

HR analytics helps to measure staff turnover, identify the ways of fostering engagement and improve employee productivity, conduct recruiting analysis from different perspectives, andevaluate talent management through analytics.

Operational and asset analytics

Operational and asset analytics help improve business processes, assess supplier and vendor relationship risks, forecast demand and optimize inventory management tasks.

Industrial analytics

Industrial analytics helps optimize production, assure product quality, and monitor equipment utilization while fostering predictive maintenance.

Want to leave all your analytics woes to us and be assured of consistent data performance for your business? Get in touch with us today for a quick discussion!