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Opolox is a big-data analytics company
focused on supporting its clients across Sub-Sahara Africa
through the use of advanced predictive modelling.

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Opolox services are underpinned by a cloud-based,
advanced and open-sourced proven technology
that enables the collation, storage and analysis of all data
forms using Opolox's technology stack,
proprietary algorithms and
advanced machine-learning tools.
The resulting analyses are delivered in a user-friendly
format that can be manipulated, enabling data-driven
and actionable decisions.

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Through working with clients,
Opolox has developed its own data analytics
and management methodology:

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  • P: predictive modelling
  • A v: analytics and visualizations
  • I: information
  • d: data

Data

The variety and volume of data generated is and will continue to increase exponentially as society develops. Our ability to capture this data has become critical. However, data with no analysis has no value; “one can have data without information but one can not have information without data”.

Storage

Data generated comes in many forms – structured, semi structured and unstructured. With this emergence of big data, Opolox supports its clients to make sense of all this data.

Knowledge

Knowledge is only gained once data that has been captured and stored, is analysed. Opolox, through its technology platform uses its proprietary algorithm coupled with advanced machine learning, to provide its clients with analytics. This resulting analytics is delivered in an easy to understand visual format which can be manipulated, enabling data-driven and actionable decisions.

Insights

Opolox supports its clients by providing predictive modelling to its analyses. This enables Opolox to mine clients old and current data to predict a future behavior through use of statistical models by forecasting probabilities and future trends.

Data is the oil of the future

Services

Data Collation

Clients typically already have some data that has been captured and stored in a variety of forms but this data is likely not being analysed for business, strategic or operational decisions. Opolox is able to support its clients to organise this data which would enable easier analysis, and more critically help develop a solution that would ensure and enable the collation of greater volume, variety and veracity of data in an efficient manner. Opolox also collates and gathers new and real-time data using its own proprietary technology for itself, as well as on behalf of its clients.

Data Storage

Storage and archiving of organisations and company’s old data: repurposing of old data into new forms, and digitising and scanning into a homogeneous form that enables the company to be able to access, search and analyse all of its critical data in one central location.

Current and new data: The variety and volume of data generated is and will continue to increase exponentially as society develops. Our ability to capture this data has become critical.

Analytics & Visualisation

Knowledge is gained once data that has been captured and stored, is analysed. Opolox, through its technology platform uses its proprietary algorithm coupled with advanced machine learning, to provide its clients with analytics and most critically insights. These resulting analytics and insights are delivered in an easy to understand visual format which can be manipulated, enabling data-driven and actionable decisions.

Predictive Analytics: Opolox supports its clients by providing predictive modelling to its analyses. This enables Opolox to mine clients old and current data to predict a future behavior through use of advanced statistical models and algorithm by forecasting probabilities and future trends.

Our Approach

Clients typically have some data that has been captured and stored in a variety of forms but is not being analysed for strategic, operational or business decisions. Most client’s data is stored as flat files, log files in a relational database, NoSQL data islands, PDF archives and more. From our experience with clients, there will likely be some element of data cleansing and organising especially for older, legacy data.

For newer data types, Opolox is able to access data including real-time data through a client’s API. Opolox is also able to gather terabytes of various data types using its own proprietary technology and application.

Customisation: Opolox provides custom solution and predictive analytics visualisation front-end for its clients.

Industries

Banking, Insurance and Pensions

Financial services firms have access to a vast treasure trove of insights locked in big data volumes (i.e. structured and unstructured data) that have been and continue to be generated during the course of managing clients and investing financial assets on their behalf. With Opolox, banks, insurance and pension companies can build predictive models that unlock intrinsic patterns in data and reveal key guidance for when to transact, how assets should be allocated, and portfolios rebalanced to meet growth targets. In addition, the ability to target clients with the right services and products at the appropriate time has become imperative to doing business successfully.

Telecommunication

Use of data analytics and machine learning has the potential to place telecommunication companies (telcos) in a prime position to win the battle for customers and create new revenue streams. It provides them with a wealth of information about their customers’ behaviors, preferences and movements. The amount of data and its complexity is now beyond the ability of humans to comprehend. In addition, fraud can impact a company’s brand as well as revenues, and fraudsters are getting increasingly sophisticated. Yet, many telcos still struggle to fully derive the greatest value from big data. Though telcos have handled large amounts of data for many years, the game-changing aspect of big data today lies in using data to derive new insights in real-time or near real-time, to become more competitive and create significant business value.

Macroeconomics

With the increasing integration of the world economy, businesses are increasingly impacted by external economic factors which can be forecasted. Economic events and trends have historical precedents, and their effects on the economic output of different industries can be accurately forecasted. With the volume and variety of data being generated and captured, this can be done efficiently, more frequently and in certain cases real-time. As a result, firms and Government now have the opportunity to accurately predict how different macroeconomic scenarios will impact business performance and economic output.

Healthcare

The promise and opportunities of big data in health care have unimaginable potential. The increasing digitisation of healthcare data means that medical health organisations often add terabytes' worth of patient records to data storage annually. Much of this data, mostly unstructured, sits unused, having been retained largely for regulatory purposes. Advanced analytics can now be used to bring this data to life through insights gained from its analyses. These insights improve delivery of medical care, enable practitioners to efficiently manage available resources, maximise patient engagement and optimise health financing and insurance provision.

Retail and Sales

Use of machine learning is pervasive in the retail sector with firms able to increase sales and customer engagement through the application of predictive analytics for item recommendations, predicting customer churn, lead and ad scoring, analysing buyer sentiment, and identifying secondary markets among many others uses. Machine learning techniques can also be used in all retail and sales environment to gain insights into sales conversion, frequency and thus enables more efficient production and stock management.

Marketing and Advertising

Big data analytics have been delivering value in the marketing and advertising sector for many years. The systems that recommend items, target ads to likely buyers and parse lists into customer segments with similar buying habits all have at their core, some application of data analysis and machine learning. In fact recommendations systems have been in use for many years and are proven to improve conversion to sales somewhere in the range of 20 to 200 percent depending on the type of business. However, with the development of new and more advanced technology, data analytics and machine learning use in marketing and advertising is not only anticipated to experience further growth but provide a significant competitive advantage to its users.

Government

There are as many applications for machine learning in the public sector as there are government agencies. Benefits of predictive analytics include policy decision-making, public safety, the police and security services and services administration. Emergence of newer applications including sentiment analysis such as mining of social media content and feeds to understand where to direct resources, analysing sensor data to make public transportation safer and implementing fraud detection systems to limit the impact of identity theft and insure proper administration of government benefits.

Security - Fraud and Forensics

Use of machine-learning in fraud detection and forensic work has increased significantly, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against fraud especially online. For fraud analysts working in companies with smaller budgets, machine-learning tools are a cost-effective way to tighten fraud controls while maintaining fast decision times.

Technical overview

Banking, Insurance and Pensions

For banks and other financial services providers, data initiatives predominately revolved around improving customer intelligence, reducing risk, and meeting regulatory objectives. However, with big data and the growth of unstructured data, combined with the increasing application and use of IoTs, the ability to capture and gain insights from the mass of data will lead to a distinct competitive advantage for the companies that are able to harness these new data assets. Technological advancements, especially the dominance of mobile as the main tool for users to transact with their banks and financial service providers, has brought to the fore both the importance and opportunity for banks and financial institutions to provide customised solutions to their clients.

Operational & Compliance

  • Forecasting
  • Probability Analysis
  • Risk Management and Analytics
  • Compliance Management

Customers

  • Customer Profiling
  • Credibility Evaluation
  • Churn rate

Fraud

  • Anomaly Detection
  • Real-time Analytics
  • Risk Analytics
  • Credibility Evaluation

Macroeconomics

Using a variety of predictive analytics tools including time series and forecasting exercises, and demand and supply models to identify historical correlation between sales and economic indices, firms can apply historical patterns to forecast future outcomes. Sentiment analysis can support macroeconomic implementation enabling the measurement of economic policies and their impact and effectiveness on the overall economy through insights gained to analyse forward-looking prices and inflation trends.

Advanced Analytical Techniques

  • Time Series and Forecasting
  • Real-time Analytics
  • Data Aggregation

Prices and Inflation

  • Prices and Inflation Modelling
  • Correlation Modelling

Behavioral Analytics

  • Sentiment Analysis
  • Social Quantification

Telecommunication

The amount of data generated by telcos and its complexity is now beyond the ability of humans to comprehend. This data provides telcos with a wealth of information about their customers’ behaviours, preferences and movements and the use of data analytics equips them to win the battle for customers and create new revenue streams. The game-changing aspect of big data today lies in using data to derive new insights, mostly real-time or near real-time, to become more competitive and to create business value.

Operational & Compliance

  • Forecasting
  • Profitability Analysis
  • Risk Management and Analytics
  • Compliance Management

Customers

  • Customer Profiling
  • Credibility Evaluation
  • Churn Rate

Fraud

  • Anomaly Detection
  • Real-time Analytics
  • Risk Analytics

Healthcare

Advanced analytics can be used across all areas of healthcare management and delivery to improve all health-related outcomes. These include and are not limited to clinical, financial efficiency and risk management.

Client Health Provision

  • Electronic Health Records (EHRs)
  • Patient Engagement
  • Health Insurance Provision
  • Health Data Acquisition

Public Health

  • Clinical Analytics
  • Chronic Disease Trends
  • Episode Analytics
  • Public Health Strategy Management
  • Population Health Analytics

Health Finance

  • Value-Based Healthcare
  • Financial Efficiency & Cost Effectiveness
  • Risk Management

Retail and Sales

Machine learning techniques can be used in retail and sales environments to gain insights into sales and purchase process including the ability to target customer using data-driven promotion and real-time analytics. Combined to this is the emergence of the internet of Things (IoT) which will play a critical role in terms of how sensor data will increasingly drive this new phase of retail and sales. An example can be a retail store offering a promotion or notifying a target of a price discount on its app on your mobile phone in real-time once it detects you are in the store.

Behavioral Analytics

  • Sentiment Analysis
  • Customer Dynamics

Optimisation

  • Lead Generation and Scoring
  • Conversion Rate Optimisation
  • Stock & Supply Optimisation

Customer Scoring

  • Propensity Scoring
  • Churn Rate
  • Customer Engagement

Marketing and Advertising

The ability to track the success of your website/app has become a standard measurement in marketing and advertising. Traditional analytics focuses on measuring key metrics and quantitative data such as users' activity overtime, the devices and OS being used, how much time is spent on each screen/page and their geographic breakdown of users. The traditional tools emphasize the numbers (and not the reasons) and can help clients answer the eternal question: What? But to understand WHY specific actions were performed by your users, you need to dig deeper into the user experience and qualitative data by using behavioral analytics. These analytics tools provide insights into what it is that works and what does not work for users and why. Knowing this enables clients to streamline their app/website optimization process.

Behavioral Analytics

  • Customer Dynamics
  • Sentiment Analysis
  • Social Quantification
  • Cohort Analysis

Customer Scoring

  • Propensity Scoring
  • Churn Rate (customer attrition)
  • Social Ranking
  • Customer Engagement
  • Credibility Evaluation

Optimisation

  • Conversion Rate Optimization
  • Lead Generation and Scoring
  • A/B Testing
  • Clickstream Analysis
  • Customer Journeys

Advanced Analytical Techniques

  • Modern Portfolio Theory
  • Markov-Chain Analysis
  • Time Series and Forecasting

Government

 

Governance

  • Policy Formation
  • Budget Planning and Forecasting
  • Resource Management

Population

  • Forecasting
  • Smarter Cities
  • Event Mitigation Planning
  • Security

Transport

  • Commuter Pattern Detection
  • Traffic Flow Analytics and Visualization
  • Queueing Forecasting

Security - Fraud & Forensics

Forensic Data Analysis (FDA) is a branch of digital forensics that utilises structured and unstructured datasets. It examines structured data with regards to incidents of financial crime with the aim of discovering and analysing patterns of fraudulent activities using application systems and underlying databases. Unstructured data in contrast is taken from communication and office applications or from mobile devices. This data has no overarching structure and its analysis therefore requires applying keywords or mapping communication patterns, usually referred to as computer forensics.

Fraud

  • Anomaly Detection
  • Visualization of large datasets
  • Pattern Recognition
  • Causality and Temporal Analytics

Forensics

  • Graph Analytics
  • Text Analytics
  • Intrusion Detection
  • Face Recognition
  • Image Analysis
Serving all 48 African countries
Crunching 2T of Data
Processing 5M Data Feeds Per Day
We are dedicated

About us

Soji Omisore

Soji Omisore

Founder & CEO

Creating connections and visions

Francois Vanderseypen

Francois Vanderseypen, Ph.D.

Data Science & Research

Turning the abstract into the concrete

Adaobi Okereke

COO

Creating smart moves in
a complex business scene

Check out some of our samplesVarious metrics visualized related to Sub-Sahara Africa.

Contact us

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Lagos Office
Greenacres
Ilabere Avenue, Ikoyi Lagos, Nigeria

Phone: +234 813 861 3711
Email: info@opolox.com
London Office
11 Dundonald Avenue
NW10 3HP, UK

Phone: +44 203 289 3600
Email: info@opolox.com

Ordinary Data. Extraordinary Insights. Exponential Growth.