Advanced Analytics

The Value of Information

Business models changed continually over the past decades: production processes accelerated, product shelf life shortened, markets became globalised, and new digital means of communication like social media appeared.

All these developments mean that companies are now increasingly seeking ways to preserve their differentiators, to retain their customers’ loyalty, to increase their market share and to penetrate new markets.

Information is key to meeting these challenges!

Companies generate, structure and store an increasing amount of data in their ERP, CRM or other enterprise systems. Differently structured data from suppliers or partners are complementing these data.

At the same time, exponential amounts of unstructured data are generated every second through email exchanges, on social media or through data sources available on the web, also known as OpenData.

Being able to extract the strategic insights out of all this data, will be of major importance for the survival of more and more companies and organisations.

What can Advanced Analytics tools bring to my organization?

Impact on costs

Identification of the hidden correlations within data series and identification of phenomena and their cause for a better understanding of the cost structure.

Impact on growth

Genuine growth driver in terms of revenue, due to improved targeting of customer profiles, better classification and appropriate offers.

Impact on processes

Improvement in the quality of both the products and services delivered thanks to a deductive approach identifying causes and effects.

Impact on productivity

Introduction of predictive maintenance (predictive analytics), reducing down time and optimizing the entire production cycle.

NRB’s Advanced Analytics Service Offering

Advanced Analytics

 

Business Intelligence

NRB can assist you in defining and implementing Advanced Analytics projects within your organisation, while using market leading tools, and the skills of its professional and technical experts. During these projects, our approach consists of four phases:

  • DEFINE : Definition and roll-out of predictive scenarios based upon open questions or assumptions to verify 
  • PREPARE : Definition and rollout of Data Models enabling the analysis of targeted data
  • EXPLORE : Introduction of tools like Data Discovery (study, implementation, coaching, maintenance and hosting in SaaS mode, etc.)
  • DESIGN & MONITOR : Definition and introduction of Dashboards providing permanent insights of the organisation’s performance.

The goal of Business Intelligence is to give to the right person the right information in the right format. Our BI services include a broad scope of business deliverables to provide our customers the insights: strategic management (dashboards), activity monitoring and operational reporting. The following services are provided to implement these deliverables: BI-strategy definition and audit, Data Governance and modelling, BI Architecture, ETL or Extract, Transformation and Loading process, and BI visualisation (dashboards, reports,). Our services are provided in the form of consultancy, implementation, deployment, training, support and platform migration services.

Challenges:
  • Well identified business value (turning data into relevant information.
  • Validated data.
  • With the right format.
  • Updated with the right frequency.
  • With the quality needed
  • With the associated governance

Business Analytics

We offer an end to end Analytics service: from the elaboration of a business case up to the implementation, and hosting of the solution and data: strategy/advisory, data exploration, data preparation, building statistical models, visualising the results, implementation of the full solution and integration of the analytics solution in the full IT landscape. Services are provided in fixed price (turn-key), T&M (Times & Material), MO (Managed Operations) and staffing approach. In general, a Business Analytics project starts small with a consultancy mission: to first understand what are the requirements of the business, to investigate the potential of the available data, to elaborate the use case.

Challenges:
  • R&D/Quality: quality issues, quality root-cause-analysis mystery.
  • Production: outages, capacity/performance issues, production process variability.
  • Operations: customer complaints, too high assets cost and performance issue, late delivery.
  • Maintenance: maintenance cost that should go down, inspection costs that should go down.

Fraud Analytics

There are many possible definitions, but for the purpose of our solution, we will consider fraud as any intentional act or omission designed to deceive others, resulting in the victim suffering a loss and/or the author achieving a gain (reimbursement or reselling of (stolen) items). Some factors that may favour fraud are: size and value of items, ease of resale, cash, lack/weak controls…

Challenges:
  • How to decrease risks and their costs associated?
  • How to predict risk before taking any action such as proposing a contract or validating a claim that implies a reimbursement?
  • How to identify patterns that can significantly increase the quality for each domain like Security?

Compliance Analytics

The purpose of compliance analytics is to ensure quality, compliancy and/or low risk across high volumes of internal text data and external text data. The value it brings is to automate verification and validation processes, speed-up knowledge work, flow and collaboration to inform decision making. It applies to admission dossiers, claim dossiers, scientific publications … and relates to many sectors.

Challenges:
  • Deal with high volumes of documents.
  • Make processes scalable and reduce manual knowledge work.
  • Match/link/check extracted information on completeness, correctness, consistency and validity of documents.

Predictive Maintenance and Quality

Organisations are harnessing big data to develop insights to fine-tune systems, inform decision making, and develop products that were previously impossible to make. However, many organisations still struggle to adopt big data and fully capture its potential.

Challenges:

As R&D, Operations & Maintenance or Inspection responsible, you need to:

  • Prevent failures by using predictive root-cause analysis on machine data.
  • Predict product quality by detecting anomalies faster and on all items.

Predictive Analytics enable Production & Maintenance Management to improve quality and service levels, while cutting production and maintenance costs.

  1. Predictive Maintenance

- Anomaly detection

- Anomaly prediction

- Health score assets

- Scenario planning

  1. Quality Analytics

- Correlation paterns between data variations and quality issues

- Suspect items identification

- Full automatic analysis on process data

 

Working together, we will use multiple and massive data sources, integrating Information Technology, Operations Technology and Business Contextual data. We will apply multivariate non-linear anomaly detection models for more refined and accurate predictions. We will ensure a smooth set-up with automatic and dynamic learning of normal behaviour, so allowing constant evolution of the solution.

Predictive maintenance and quality

We help you building a data driven organisation by leveraging Big Data and Analytics Platforms managed by data scientists. The iterative deployment model allows fast set up, quick wins and long lasting effects.

Technology

In us they trust...

Contact us :

  • Mail us
  • Phone us

Follow us :

  • Linked In
  • Twitter
  • Google +
  • Slideshare
  • Youtube
  • Flickr

Contact us

Service Desk

t. +32(0)4 249 77 77

Liège

t. +32(0)4 249 72 11

Brussels

t. +32(0)2 286 57 11

Send us a message

Partnerships & certifications

  • SAP Partner
  • Microsoft Gold Partner Certificaton in Applcation Development
  • ISO:9001
  • ISO:27001