Data Analytics & Management Services
Empower your organization and rule your data world with industry-leading data analytics solutions
Unlock the Strategic Value of Data For Your Business Growth
Data has become a major asset for many businesses, allowing them to boost revenue and improve their processes. In addition, digital platforms and applications are only useful if they can provide value-added functionality based on insights derived from historical and real-time data.
As a data analytics services company, Aiolane helps businesses unleash their data capital and gain valuable insights about their customers, operations, and products from a variety of data sources.
Our data engineers apply advanced big data and business intelligence tools, as well as innovative techniques such as artificial intelligence (AI) and machine learning (ML), to optimize processes and unlock new market opportunities for your organization.
What we do
Data Governance
Data Governance
With our robust data monitoring and management processes, you can stay in compliance with all relevant regulations and gain control over your data at every step of the journey.
Data warehousing
Data warehousing
We help your company better manage and leverage data by providing a more powerful, scalable, and robustly architected single repository to handle all your disparate and disconnected data sources.
Data Migration
Data Migration
We solve the logistical challenges of selecting, preparing, extracting, and transforming data by applying experience and security best practices while limiting the impact on operations.
Data Science & Machine Learning
Data Science & Machine Learning
Drive decision-making and enhance optimization by implementing data science best practices, from machine learning and deep learning technologies to artificial intelligence and cognitive computing.
Data Security
Data Security
Maximize protection against data breaches and other malicious activity by standardizing and automating security, streamlining internal training programs, and ensuring efficient backup and recovery processes.
Data Analytics Consulting
Data Analytics Consulting
Aiolane experts can help you find the right data strategy and guide you through the process of planning, designing, implementing, and optimizing your custom data analytics solution.
Data Monetization
Data Monetization
Leverage your data to generate new revenue streams by implementing Aiolane data monetization practices that focus on creating measurable revenue and business impacts.
Master Data Management
Master Data Management
Make sure your company's master data is consistent and accurate, and create a centralized master data management hub to increase internal productivity and improve customer experience.
BI & Data Visualization
BI & Data Visualization
Deliver actionable enterprise-level insights across your company with well-designed visualizations, interactive reports, and intuitive dashboards that make analytics clear to everyone.
Our data analytics process
Determine the objective
First, our experts assess your company's environment to identify critical goals and evaluate how these goals can be achieved.
Gather data
Once the goals have been defined, we begin to collect and organize relevant data from internal and external sources.
Prepare data
Our team then evaluates data quality, as well as eliminates errors, replicas, nonessential data points, and other data issues.
Perfom data analysis
After data cleaning, we use various data analytics techniques to understand, interpret, and draw conclusions based on your project requirements.
Share the results
Finally, the results of the data analysis are presented in the format required by the users, and any necessary adjustments are made.
Business Benefits
Better-Decision Making
With more actionable and valuable insights gained through data analytics, businesses can improve their decision-making and project management, and anticipate market needs by delivering the right product before it is requested.
Optimized Operational Efficiency
Applying advanced analytics can assist organizations in improving business operations, productivity, and efficiency, as well as optimizing their workforce based on their business and customer needs.
Enhanced data security
In today's world, every organization faces data security threats. Using data analytics can help organizations strengthen their overall cybersecurity posture and avoid serious financial or reputational damage in the future.
Improved Customer Experience
Finally, data analytics helps organizations gain insight into customer behavior to provide a more personalized experience and improve customer satisfaction.
Our clients
DATA ANALYTICS FAQ
What is data analytics used for?
Data analytics is the process of cleaning, preparing, transforming, modeling, and analyzing raw data. Its main goal is to help businesses better recognize their customers, evaluate marketing campaigns, personalize content, build content strategies, and improve their products.
What are the four types of data analytics?
Depending on the workflow stage and project requirements, there are four main types of data analytics that provide varying depths of analysis: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics.
Descriptive analytics answers the question “what happened?” by analyzing past data, identifying certain patterns within it, and presenting the report in the form of pie charts, column charts, tables, dashboards, or line charts. It is the most basic sort of analytics, and it is primarily used to track key metrics and measures within an organization.
Diagnostic Analytics.
Diagnostic analytics is a more advanced form of analytics. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Diagnostic analytics allows you to dig deeper into the insights generated by descriptive analytics and get to the root of an organizational issue.
Predictive Analytics.
Predictive analytics is mainly used to predict future trends or events, trying to answer the question “what is likely to happen?”. Using previous data, it uses techniques such as pattern matching, forecasting, multivariate statistics, regression analysis, and predictive modeling to make predictions about future outcomes.
Prescriptive Analytics.
The final and most complex type of data analytics is prescriptive analytics. Combining the findings of all previous analyses, it answers the question “What should be done?” and determines the best actions an organization should take to solve its problems. Prescriptive analytics uses sophisticated techniques such as complex event processing, recommendation engines, machine learning, graph analysis, heuristics, simulation, and neural networks.
What does a robust business strategy using analytics look like?
A data-driven business strategy is a series of concise, actionable best practices for leveraging the insights gained from data. An effective and efficient strategy involves making the best use of a company's existing data resources and helps find solutions to some of the company's most pressing problems.
When is the best time for businesses to implement an analytics strategy?
Most organizations turn to analytics when faced with a problem, believing that the solution can be found in their data. Data analytics, however, is not a one-time activity, but an ongoing process. Companies should not lose sight of analytics and should plan to use it as a normal business function. Once companies realize the potential of analytics to solve problems, they begin to use it for all types of strategic and general business decisions.