Data preparation is the process of collecting, blending, organizing, and structuring data to make data analysis outcomes better and fast. It is a critical component of data analysis as it weeds out all the unimportant and irrelevant elements in datasets. Good data preparation makes it possible to have data that do not have any calibration issues or discrepancies between datasets and thereby deliver insights that are on track.
Outsource2india has over two decades of experience in data preparation services. We are driven by a team of experts that primarily includes web technicians, database administrators, systems programmers, and analysts. The team takes care of the process of sifting, formatting, verifying, and keying data correctly so that only high-quality data is available for analysis. Our clients have leveraged our services to enhance efficiency and experience higher productivity, through accurate querying, profiling, reporting, and sharing of high-quality data.
As a leading data preparation service providing company, we understand each client's unique business requirements and provide clients with customized services. As a well-known data preparation services providing company, we provide our range of services to our clients. Some of these services include -
Data generated from different sources contain a lot of unwanted elements that must be weeded out to ready it for proper analysis. Data cleaning services entails correcting common problems and other errors in data. It is an operation performed in the first stages of data preparation. The objective is to make the data less messy and more useful. We leverage our domain expertise to identify misfit, messy, corrupt, or erroneous data and rectify it.
Data tend to have wrong values, because of reasons such as incorrect typing, duplicated data, corrupted data, and so on. We correct or prepare the data using various methods such as statistics to differentiate normal data from outliers, identify redundant rows of data and remove them, mark empty values, impute blank values using learned model or statistics, remove duplicate rows and columns.
The objective of data analysis is to develop models that assist in making a prediction. Feature selection is a technique that involves picking a group of input features that can take the place of a variable target and assist build a prediction model. This is an important part of the preparation process as irrelevant or redundant variables can mislead the algorithms completely and lead to improper predictions.
Our feature selection technique banks on groups that use both the target variable and those that do not. The target variable is broken into further groups that select features automatically to fit the model; select features to develop the best performing model, and give a score to each feature so that a close performing subset can be selected. We primarily bank on statistical methods for finding input features. The right method is chosen based on the input variable data types and the best possible statistical methods to be used.
This is a preparation stage in which a change in the data variable distribution is carried out. We make use of a range of techniques to transform data and apply it to output and input variables. Data may be categorical or numeric, with variable subtypes for each. At this stage, a numeric variable is either converted to an ordinal variable or code as a categorical variable as Boolean variables or integers.
We specialize in Discretization Transform, Ordinal Transform, or One-Hot Transform. In Discretization transform, we code a numeric variable to ordinal. In Ordinal Transform we code a categorical variable into integer and in One-Hot Transform, we code a categorical variable into binaries.
This a type of data preparation in which new input variables from the available data are created. Our subject matter experts identify new features that can be interpreted from the data. A common approach is to create copies of numerical input variables with a simple mathematical operation, such as multiplying them with other input variables or raising them to power.
Feature engineering is carried out to add a broader context to a single observation. Sometimes it also helps in breaking down a complex variable and providing a more straightforward and simple perspective on the input data.
The dimensionality of Data is the number of input features for a dataset. In this type of data preparation, the inputs can be scaled up or scaled down to any number of variables to create volumes of different dimensions. Unlike feature selection, the input variables, in this case, are not directly related to the original input variables. This makes the projection a bit hard to interpret. The big advantage of this technique is that it removes linear dependencies between correlated variables.
As the name implies, dimensionality reduction is the reduction of data from a high-volume space into a low-volume space so that the low-volume representation has some relevant and meaningful properties of the original data. We leverage the two most common approaches to dimensionality reduction i.e., Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).
Our data preparation process depends on the kind of data that needs to be parsed and analyzed. The following steps define the process -
In this stage, we take the right steps to understand and define the data problem that is to be solved. We look for a problem that is clear, concise, and measurable. Understanding this in the right perspective helps in the ways to collect data, identify data patterns, and show correlations and predict outcomes
The problem identification process is followed by the collection of the right data. The collection is usually done from an existing data catalog or added ad-hoc. We collect data from first-party, second-party, and third-party sources. However, first-party data helps in laying the foundation of the dataset. Additional data types help in increasing the scale of the audience or help identify new target audiences
Once the problem is identified we choose the data science algorithms that need to be applied to data. This helps in roughly grouping the data into families such as two-class classification, multi-class classification, clustering, regression, dimensional reduction, reinforcement learning, etc.
Data cleaning is the most time taking part in the data preparation process. But it is extremely crucial as it entails removing faulty data and filling in gaps. In this process, we remove extraneous data and outliers, replace all missing values, standardize data to a particular pattern, hide sensitive or private data entries, etc. After cleaning the data we test it for errors that might have cropped up and resolve the issue
In this process of data preparation, we change the format to make the data consistent so that a well-defined outcome can be reached. This makes the data easily understood by a wider audience. An integral part of this stage is to add and connect data with other pertinent information to get deeper insights
After all the steps are addressed sequentially, the data is stored in a third party application. This is primarily a business intelligence tool thereby preparing the stage for processing and analysis of the data to take place
We are a result-oriented data preparation service provider and offer our clients a range of benefits for outsourcing their data preparation requirements. These include -
We provide our clients with highly flexible pricing options that will suit their business requirements and need perfectly. Our pricing packages are designed and tailor-made to suit their client's requirements perfectly.
Outsource2india is an ISO/IEC 27001:2013 ISMS certified firm. This ensures that all the data that you share with us will be completely safe and not divulged to any third party. The data is handled only by authorized personnel at all times.
Our team consists of core data preparation experts with more than 5 years of experience in restructuring unstructured data environments. They have wide experience in preparing data for discovering sustainable solutions that the needs of organizations. This includes complex multi-tier data.
Data preparation is very time-consuming. Analysts need to spend as much as 80 percent time cleaning and preparing data. Most of the time this ties up key people and engages people with the very basic but grueling and laborious task. As we have mastered the task of data preparation and analytics we provide data to business analysts in a short turnaround time.
We have catered to the data preparation requirements of a wide variety of organizations across different industry verticals. As a result, we understand what it takes to identify and fix errors quickly and clean and reformat datasets to ensure that only high-quality data is available for analysis. As a result, our clients have always been able to make robust business decisions.
We use best-of-breed applications to deliver the best solutions to our clients. We know tools such as Altair Knowledge Works. Alteryx, Data3Sixty Analyze, Datameer, Tamr, Paxata, TMMData each of which have engaging visual interaction and pattern-detecting intelligence. This ensures that we can make the overwhelming task of data preparation more fast, accurate, and robust.
We specialize in preparing data in the cloud. This assists in superior data scalability. With our cloud data preparation services, data preparation requirements can evolve at the pace of the business. By leveraging this service organizations do not have to worry about the underlying infrastructure of data preparation or try to figure out matters related to its evolutions. Besides it leads to accelerated data usage and collaboration as it obviates the need for technical installation, and enables teams to collaborate for faster results.
We guarantee our clients a great return on investments with high-cost savings on software licensing; reduced time on data preparation, improved data quality, increased business scalability, and accelerated time-to-market for data products. Our services have also ensured that our clients see financial results in the fastest possible time.
Our data preparation services are highly sophisticated and help our clients get access to critical insights fast. By acquiring insights into how people and assets are performing; the possibility of different events disrupting growth, and other crucial pinpoint bottlenecks and growth opportunities we have assisted our clients to surge ahead in the competition.
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Outsource2india has over 22 years of experience in assisting its clients in agile data preparation services and a plethora of other data processing services. Our data preparation entails ingesting raw data from multiple sources, refining the data by cleansing, formatting integrating, and masking data by applying data quality rules and catering it for analytics consumption. Over the years we have assisted organizations to improve data quality, establish a strong data catalog, increase user efficiency and agility, streamline operations, get a holistic view of performance and production and improve data governance. If data is the new oil, we ensure it becomes central to the way organizations leverage it and do their business.
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It's a process of cleansing and polishing the raw data so that it can be used for processing and analysis in a data table or a consolidated file to improve business intelligence.
It fuels the analytics engine that needs dynamic data to streamline the business process. It helps data scientists and researchers to develop models from training data that is carefully prepared.