Making informed business decisions is vital to the growth of your company. Here at YittBox, we specialize in many different data analysis tools to help you with that. Using a variety of these tools, we have the knowledge and capability to transform your raw data into useful information that will help you draw clear, concise conclusions about your business. Through a variety of data analysis processes, we can ensure your company will gain a greater understanding and superior awareness of the data assets inside of your walls that go unused today.
With our expertise, we will take your raw data and summarize it into a customized report or data visual. This assists in pinpointing patterns and trends that will help you make sensible and reliable decisions for your organization. Analyzing past and present data can help you make predictions about what will be most productive in the future. Let your past work for you!
At YittBox, we know all data doesn’t look the same. Which is why we provide expert-level services across multiple data analysis tools, including:
The use of Excel is widespread in the industry. It is a very powerful data analysis tool, and almost all big and small businesses use Excel in their day-to-day functioning. the powerful features Excel has to offer to analyze data
It seems Microsoft has been running wild recently, throwing “Power” before and “365” after many of their products! For those new to the “Power” suite of Microsoft data collection and analysis tools, it can be difficult to understand exactly what each product does. However, an understanding of the role that each has to play is critical for companies looking to get the most out of their data.
We’ll start with Power Pivot; the engine of the entire operation. Also known as the DAX Engine (after its underlying language – Data Analysis Expression), Power Pivot is a baked-into excel feature that enables users to build a data model, create relationships, write calculated columns, and measure formulas from a variety of sources.
In short, Power Pivot extends the capabilities of Excel by allowing for the import of larger data sets from various sources and the creation of more sophisticated data models with DAX.
From Power Pivot you can create visualizations in Excel, Power View, and Power Map. Excel allows for the Pivot Tables that we all know and love, while Power Maps is a geography-based visualization tool. Finally, Power View is an explorer that shows data on interactive dashboards.
Power Pivot first appeared in 2008, when you had to download a free add-on for Excel. From 2016 onward it can be found in Excel.
PowerQuery is a tool which enables users to retrieve, extract, and shape data prior to bringing it into Excel (and/or into the PowerPivot data model). Users can insert and remove columns, filter, and sort tables, and change data types, for example.
Power Query runs on the “M” Language (most-likely named for what it is: Mashup Query Language). Now simply labelled as “Get and Transform,” it can be found under the “Data” Ribbon in Excel.
It should be noted that Power Query is an optional tool. When importing data into Power Pivot you have two choices. You can either import it directly into Power Pivot or you can channel it first through Power Query. Your choice depends on how noisy your initial data is.
Microsoft’s Power BI is a business analytics and visualization solution built on the Power Pivot and Power Query engines. Users can use “Get Data” (Power Query) and “Data Model” (Power Pivot) to shape and analyse their data. They can then create interactive dashboards and reports; visualizing their data with Power BI’s every increasing number variety of visualizations. In fact, developers can also build custom visualizations to suit the individual needs of their companies. The uber data geeks out there can even use R scripts for custom visuals.
At Tableau, we believe in using the visual to drive your analysis. When you get observable feedback as you analyze, the power of exploration is in your hands. When you build an analysis and learn from it simultaneously, opportunities for investigation present themselves. Tableau’s visual analytics makes asking and answering questions of your data intuitive, even as those questions naturally grow in complexity—so you can continue to ask, “Why?”
Tableau fuels unlimited data exploration by taking advantage of your natural ability to spot visual patterns quickly. It's all thanks to VizQL, our patented technology that translates drag-and-drop actions into data queries. In other words, we built analytics that work the way you think.
We believe that to help everyone see and understand data, we need to provide rich capabilities for users of all levels of technical ability. From simple, go-to metrics to advanced analytic techniques, Tableau provides a flexible front-end for data exploration with the necessary analytical depth for the data scientist. By leveraging sophisticated calculations, R and Python integration, rapid cohort analysis, and predictive capabilities, data scientists can conduct complex, quantitative analyses in Tableau, and share visual results to facilitate better understanding and collaboration with data.
Very simply, Microsoft Access is an information management tool that helps you store information for reference, reporting, and analysis. Microsoft Access helps you analyze large amounts of information, and manage related data more efficiently than Microsoft Excel or other spreadsheet applications.
All of the things people use as arguments against using MS Access turn out to be reasons for others to use it.
“It’s super easy to use” - Of course, it is. Setting up tables, queries, forms, and reports is a veritable walk in the park. You can import data from spreadsheets, text files, or other more suitable data sources like SQL Server.
“It’s not big enough and my database just kept growing” - Over time you will eventually max out its size limits but for small scale datasets and quick analysis jobs this will give you more than enough space for very little server space overhead and all you need is MS Access installed on your machine. This should come as standard with most MS Office installs so no fighting with the IT Department to install unwelcome 3rd party software.
“It’s not secure enough” - We advise not to put business-critical or top-secret data on it in a publicly accessible place. You don’t need a full user access infrastructure to get started, load it up, fire in your data, run your queries or VBA and it’s all there in front of you.
Informatica Analyst is a web-based application client that analysts can use to analyze, cleanse, standardize, profile, and score data in an enterprise.
Depending on your license, business analysts and developers use the Analyst tool for data-driven collaboration. You can perform column and rule profiling, score carding, and bad record and duplicate record management. You can also manage reference data and provide the data to developers in a data quality solution.
We use The Analyst tool to help you store projects, folders, and data objects in the Model repository. Then, connect it to the Model repository database to create, update, and delete projects, folders, and data objects.
SQL Server Management Studio (SSMS) is an integrated environment for managing any SQL infrastructure. Use SSMS to access, configure, manage, administer, and develop all components of SQL Server, Azure SQL Database, and SQL Data Warehouse. SSMS provides a single comprehensive utility that combines a broad group of graphical tools with a number of rich script editors to provide access to SQL Server for developers and database administrators of all skill levels.
SQL Server Management Studio for Business Intelligence To access, configure, manage, and administer Analysis Services, Integration Services, and Reporting Services, use SQL Server Management Studio. Although all three business intelligence technologies rely on SQL Server Management Studio, the administrative tasks associated with each of these technologies are slightly different.
Managing Analysis Services Solutions Using SQL Server Management Studio. SQL Server Management Studio enables you to manage Analysis Services objects, such as performing back-ups and processing objects.
Management Studio provides an Analysis Services Script project in which you develop and save scripts written in Multidimensional Expressions (MDX), Data Mining Extensions (DMX), and XML for Analysis (XMLA). You use Analysis Services Scripts projects to perform management tasks or re-create objects, such as database and cubes, on Analysis Services instances. For example, you can develop an XMLA script in an Analysis Services Script project that creates new objects directly on an existing Analysis Services instance. The Analysis Services Scripts projects can be saved as part of a solution and integrated with source code control.
MySQL Workbench includes various tools for both DBAs and Developers related to viewing and improving performance. The Performance Dashboard and reports allow DBAs to easily view overall server performance, and various reports provide views of IO hotspots, SQL statements, Network, Data Engine, and more. For developers, MySQL Workbench provides easy-to-understand views into optimizing queries and data access.
The Performance Dashboard provides quick "at a glance" views of MySQL performance on key server, network, and InnoDB metrics. Simply mouse over various graphs and visuals to get added details.
Over 20 reports help to analyze the performance of your MySQL databases. Targeted reports make analyzing IO hotspots, high-cost SQL statements, Wait statistics, InnoDB engine metrics. MySQL Workbench leverages the SYS views on the Performance Schema.
The explain plan shows the operations MySQL performs when it runs SQL statements. This information can help optimize SQL performance. MySQLWorkbench Visualize Explain plans graphically show and highlight how SQL statements execute within MySQL. By showing developers costs and tuning hints, MySQL Workbench improves and simplifies SQL statement performance tuning.
Query Statistics provide instant statistics on SQL executed from the Workbench Editor, such as details about the fields in your result set and key performance statistics from your query, such as client timing, network latency, server execution timing, index usage, number of rows scanned, joins, use of temporary data storage, and more.
Business analysts help guide businesses in improving processes, products, services, and software through data analysis. These agile workers straddle the line between IT and the business to help bridge the gap and improve efficiency.
Business analysts (BAs) are responsible for bridging the gap between IT and the business using data analytics to assess processes, determine requirements and deliver data-driven recommendations and reports to executives and stakeholders.
BAs engage with business leaders and users to understand how data-driven changes to process, products, services, software, and hardware can improve efficiencies and add value. They must articulate those ideas but also balance them against what’s technologically feasible and financially and functionally reasonable. Depending on the role, you might work with data sets to improve products, hardware, tools, software, services, or process.
Execution plans are a visual representation of how a database engine executes a query. They basically let you peek under the hood and see how the information sausage is made. Execution plans can tell you a lot about the efficiency of a query and are the main tool for troubleshooting a slow or underperforming query. Reading and understanding them can help you tune queries without messing up performance. Learn how to pull an execution plan, read one, and tune it to increase query performance.
Creating a backup database is crucial in case your first one is corrupted or damaged in some way. There are different types of backups, and knowing which to use and how to institute (and restore) each is an important part of database management.
Indexes can speed up performance by making data quicker to find, but poor indexing is also one of the biggest performance killers. Learning how to identify good candidates for indexes, as well as how to craft and maintain them, will help you run a quick and orderly database.
Online Analytical Processing (OLAP) describes a class of database applications that allow you to analyze data faster and in more innovative ways than you can with just a simple two-dimensional spreadsheet. It used to be done primarily in the form of OLAP cubes but has evolved in recent years to include running OLAP workloads directly on columnar databases.