Big Data, BI and Advanced Analytics

Business intelligence deals with the present, while data analytics is more focused on the future.

Overview

Our client needed to develop a standardized approach to integration where a new business should smoothly fit into the existing IT environment, in addition to a 360-degree customer view and a conglomerate-wide loyalty program.

Nikolaos Papadopoulos

Sr. Data and BI Analyst / fairsystems
Working with data in the modern world is far from a single action or even set of actions. Organizations now break up the process into many pieces because there are numerous responsibilities along the way. Competent data warehousing methods can ensure that information isn’t lost. Skillful analysis will try to avoid problems like social and statistical biases, over-and under-fitting, duplicability failures, and self-reference. Good business intelligence usage can ensure that information gets into the hands of decision-makers and powers a data-driven culture.

The Client

Our client, a conglomerate running 30+ businesses worldwide, such as oil and gas, travel, FMCG wholesale, and distribution, with the total revenue measuring in billions of dollars, was operating multiple businesses that are completely different in nature and totally independent in terms of IT infrastructure. Our client had operational data locked within each business, hence the absence of data integration among the businesses hindered him from implementing such initiatives as bird’s eye view reporting that would embrace all business directions, 360-degree customer view, and a conglomerate-wide loyalty program.
Roadmap
1
Assess
The project started with onsite visits to the client, where our BI consulting team had Q&A sessions with the top management of each business. During the sessions, our consultants examined the as-is infrastructures and the existing processes of each business, for example, sales pipelines functioning, as well as identified problems and integration gaps. Our team gathered the following stakeholder requirements for the future analytics and reporting tool: near real-time data processing, multi-dimensional data modeling, fast ad-hoc reporting, and mobile view for reports.
2
Deliver
Based on the findings in the assessment phase, our consultants designed a high-level architecture with multiple technology stack options. To justify the technology selection for each architecture component, our team conducted a deep analysis and produced a report justifying the choice of technology from multiple angles. The final list of implemented technologies included 16 different tools.
3
Continue
At the end of the project, our client received a top-level solution that ensured bird’ eye view reporting and the much-needed integration among disparate businesses. The solution allowed the business to scale easily thanks to a standardized approach to integration. In addition, based on our knowledge of multiple industries, our team also suggested enriching the solution-to-be with advanced business intelligence and analytics capabilities. For that, we delivered a proof of concept for a recommendation engine (the predictive model behind the engine was to boost the client’s cross-selling and up-selling opportunities), as well as a time-series prediction model to forecast sales.
Solution Details

@fairsystems, we know that challenges are merely opportunities in disguise.

360-degree customer view across all channels
Deliverable: Having all his data integrated, our client was able to:
  • Analyze his customers’ behavior and shopping preferences;
  • Assess his customers’ recency, frequency, and monetary value;
  • Identify his top customers.
Stock management optimization
Deliverable: Instead of having a shared document depicting the stock level and the necessity of constant clarifications on the phone, our client is now able to track the actual stock level both at the warehouse and in the stores almost in real-time. This transparency in the stock level has also positively influenced the ordering and logistics processes.
Data analytics
Deliverable: The implemented analytical solution consisted of the following components:
  • A data hub to store both structured and unstructured data from 30+ data sources;
  • About 200 ETL (extract-transform-load) processes;
  • A data warehouse to combine and aggregate data;
  • An analytical server with 8 OLAP-cubes and about 75 dimensions overall;
  • Reporting.
We split the implementation process into several releases to ensure that our client could benefit from interim deliverables. Overall, we developed 110+ reports for our client's different business directions and user roles.
User access control
Deliverable: To ensure data security, fairsystems also elaborated on user access control. We analyzed the highly flexible and tunable access model envisaged by our client before the project's start and came to the conclusion that it would be unsuitable, as it would negatively affect the analytical solution’s performance (it would take too long for the system to produce the desired reports). Therefore, we recommended a less complicated, though still highly efficient 3-level access model (for a business unit, a department, and a certain employee). The implemented model didn’t have any negative impact on the system’s work.
Data quality
Deliverable: As data integration from multiple systems is useless without a well-established data quality management process, our team came up with the rules applied during the ETL processes and intended to:
  • Merge master data like customer profiles from different systems;
  • Bring data to one format (for example, to have either ‘male’ or ‘female’ instead of ‘1’ and ‘2’, ‘M’ and ‘F’, ‘m’ and ‘f’ values taken from the sources systems).
With these rules, our client had his data management process running mostly automatically. However, manual interventions by a data steward were still possible.
Retail analytics (both online and offline channels)
Deliverable: Our client was able to analyze the following:
  • Traffic and conversion rates (i.e. most/least visited pages, pages with no traffic, pages with high traffic but low conversions);
  • Online store visitors’ engagement;
  • Wish list products, sales, and cart abandonment.
Employee performance
Deliverable: With KPIs and goal management reports, our client was able to redefine the employees’ quality of work.
Solution support
Deliverable: In the course of the delivered data analytics services, our team also provided our client with comprehensive support. For example, we provided training on configuring and working with OLAP cubes and adjusted ETL processes after our client’s third-party analytical vendors introduced some changes on their side.

Result:

With the developed analytics solution, our client benefited from a 360-degree customer view across all channels and business directions, as well as robust retail analytics, which allowed him to create a personalized customer experience. Our client was also able to optimize internal business processes by improving stock management and assessing employee performance.
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Churn rate decrease

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Cart abandonment rate decrease

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Acquisition and retention costs decrease by segment

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Customer Lifetime Value increase by segment

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Net Promoter Score (NPS) increase

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