Data Advisory
Good data is a founding block of good enterprise intelligence system and can help business make better decisions. Companies can leverage BI to provide performance and competitor benchmarks to make the organization run smoother and more efficiently.
We design build and implement strategies that can help enhance business performance. Our services include:
1. Business Intelligence development trainings
2. ETL processes development and documenting
3. Data governance and modelling assistance.
Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions. As part of the BI process, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data and create data visualizations, BI dashboards and reports to make the analytics results available to business users for operational decision-making and strategic planning.
The ultimate goal of BI initiatives is to drive better business decisions that enable organizations to increase revenue, improve operational efficiency and gain competitive advantages over business rivals. To achieve that goal, BI incorporates a combination of analytics, data management and reporting tools, plus various methodologies for managing and analyzing data.
BI data can include historical information and real-time data gathered from source systems as it's generated, enabling BI tools to support both strategic and tactical decision-making processes. The right BI platform can blend multiple data sources into one report and analysis: enhancing business insights and better-informed decision-making.
ETL - Extract, Transform, and Load
As the amount of data, data sources, and data types at organizations grow, the importance of making use of that to derive business insights grows as well. ETL, which stands for extract, transform, and load, is the process data engineers use to extract data from different sources, transform the data into a usable and trusted resource, and load that data into the systems end-users can access and use downstream to solve business problems.
Extract
The first step of this process is extracting data from the target sources that are usually heterogeneous such as business systems, APIs, sensor data, marketing tools, transaction databases, and others.
Commonly used Data Extraction methods:
Partial Extraction – The easiest way to obtain the data is if the source system notifies you when a record has been changed.
Partial Extraction with update notification - Not all systems can provide notification in case an update has taken place;
however, they can point to those records that have been changed and provide an extract of such records.
Full extract – Certain systems cannot identify which data has been changed at all. In this case, a full extract is the only possibility to extract the data out of the system.
Transform
The second step consists of transforming the raw data that has been extracted from the sources into a format that can be used by different applications. In this stage, data gets cleansed, mapped and transformed, often to a specific schema, so it meets operational needs. This process entails several types of transformation that ensure the quality and integrity of data.
Load
Finally, the load function is the process of writing converted data to a target database. Depending on the requirements of the application, this process may be either quite simple or intricate. Each of these steps can be done with ETL tools or custom code.
Data Governance and Modeling
Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. It establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organization. Data governance defines who can take what action, upon what data, in what situations, and using what methods.
A well-crafted data governance strategy will explain how your business benefits from consistent, common processes and responsibilities. It ensures that roles related to data are clearly defined and that responsibility and accountability are agreed upon across the enterprise. A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities.
Besides more accurate analytics and stronger regulatory compliance, the benefits that data governance provides include improved data quality; lower data management costs; and increased access to needed data for data scientists, other analysts and business users. Ultimately, data governance can help improve business decision-making by giving executives better information. Ideally, that will lead to competitive advantages and increased revenue and profits.
A comprehensive and optimized data model helps create a simplified, logical database that eliminates redundancy, reduces storage requirements, and enables efficient retrieval. It also equips all systems with a ‘single source of truth’ – which is essential for effective operations and provable compliance with regulations and regulatory requirements.
