Big data have changed the rules of the game. Keeping connected with the data becomes increasingly difficult in the metadata-driven world. In an attempt to keep pace with constant changes, BI and analytics tools are getting increasingly automated.
Generating useful information from piles of raw data and making it report-ready is what businesses of today crave. And that is exactly what we at Minerra offer: self-serving BI and analysis solutions, along with expert training and consultancy.
Why is data integration so important?
There are multiple definitions of “data integration,” which can often cause confusion if left unexplained. According to IBM, data integration is “the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information.”
No matter the definition, the key factor of the process is getting “meaningful information.” Namely, unless refined and sorted, raw data are unusable. In order for data to be analysis- and report-ready, they needs to be structured, comparable and understandable at a glance.
Minerra offers two automated solutions to that end: Ajilius (data warehouse automation tool) and Yellowfin (BI and analytics platform). Ajilius integrates data, and Yellowfin generates shareable interactive dashboards that allow for adding custom values.
Data warehouse automation does not only step up data processing, but it also generates alerts when the thresholds are met or have failed. And those thresholds are defined by you.
Data integration and business analysis
Only structured data are usable in business analysis. Because they comprise meaningful information, they are easy to compare, analyse and share.
However, the result varies depending on the data collection methods used. All of the methods compile data into an integrated view, but that is where their similarities end. Data solutions are as diverse as the information they treat, and with the rapidly changing markets, new requirements keep them evolving.
Common data integration techniques
Data consolidation compiles data from various sources and multiple systems, and then stores them in a single data storage. The technique is based on the traditional ELT technology, which extracts data from multiple sources, transforms them into a usable format and finally transfers them to a single data warehouse.
Data virtualisation provides a real-time data view through an interface. The technique pulls data from multiple sources, unifies them and displays them in an integrated format. Data are accessible from one location, but they are not physically stored there (they remain scattered).
Data federation is similar to virtualisation, as it uses a virtual database to create a common pattern for all pieces of data located in multiple sources. Also, similarly, data are brought together and displayed from a single location. The data generated in such way can be analysed through various specialised apps.
Data propagation copies data from one location to another. Data propagation can be performed in two ways: synchronously or asynchronously. Synchronous data propagation supports a two-way data exchange link (a source-target link).
Both enterprise application integration (EAI) and enterprise data replication (EDR) technologies are based on this method. EAI uses application systems for data exchange and is often applied in real-time processing. EDR transfers large volumes of data and uses logs to track the changes.
Data warehousing (DWH) is a process of compiling, cleaning, transforming and storing data in a single warehouse. Data generated in such way are presented in an integrated view and are report- and analysis-ready. In the case of automated DWH, warehouses are usually shareable as-is.
What is a data warehouse? A data warehouse is a compilation of data derived from multiple sources (both internal and external) designed to allow for data consolidation, analysis and reporting.
Generally speaking, DWH tools comprise everything from Excel spreadsheets to OLAP (online analytical processing) to data mining tools to performance dashboards. Data mining tools use data to consolidate information on multiple levels, as to define emerging trends. OLAP tools analyse metadata with the same goal. Performance dashboards summarise data and measure KPIs.
Data warehouse architecture may vary, depending on the type of the technique used. Most common architecture schemas include the star, snowflake and fact constellation. The star schema is the simplest, where the centre displays a fact table and the points display dimension tables. The snowflake schema is an upgraded version of the star schema. Namely, whereas the star schema displays de-normalised dimension tables, the snowflake schema shows normalised ones. Finally, the fact constellation schema displays multiple fact tables and multiple dimension tables.
Minerra offers some of the finest BI and analytics platforms that generate interactive dashboards, instantaneously shareable and understandable at a glance.
Not only are Minerra’s DWH tools fully automated, but they are also customisable. We cater to each customer’s specific needs and provide just the solution to boost their business operations. All of our BI and analytics platforms have one thing in common: they allow for adding descriptive attributes that make browsing as easy as it gets. I.e., you will be able to search for top customers by revenue, overall revenue trends, revenue by periods, and so on.
Choosing optimum data solutions
There is no universal advice when it comes to optimum data solutions, as every business has its respective goals. However, what is certain to benefit every company is data warehouse automation. One does not have to be an expert to realise that big data spell innovative approaches.
With everything being metadata-driven, the challenge of data consolidation becomes increasingly difficult. If we add to that client information, social networks and all kinds of smartphone apps, it becomes painfully clear that manual ELT methods simply cannot keep up anymore.
What we at Minerra offer is cutting-edge BI and analysis technology that will help you make the best data-driven decisions. Coupled with expert BI consultancy and training, you will learn how to make the best out of the messy data that keeps piling up.