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Top Self Service Data Preparation Technologies

Data will speak to you if you are willing to listen.

                                                                                                                     - Peter Drucker

Efficient analysis of business decisions and actions depends on the effectiveness of the data collected. Business intelligence is initiated through preparing data before visualizing it. A well-prepared data will result in a clean and accurate visual, which will enhance the overall performance of the enterprise. Being an integral part of the process of gaining insights, it becomes a crucial ingredient for taking actionable decisions. Data-driven decision making is always understood and appreciated. Data can be small and simple, or it can be huge- either way, it needs to be prepared with great attention to details so that the insights can be worked upon and managed.

Self- service data preparation has helped businesses to harness analytics to their advantage with the aid of technology.  Through technical methodologies and software, the slow and tedious process of data collection and preparation has evolved into shorter durations and less complex procedures. Messy data can be transformed into structured and organized data. In the pursuit of making their data clean, relevant and applicable- enterprises get stuck with seeking new technologies which may or may not assist them in the same.

Self-service data preparation tools automate actions to accelerate data preparation with the help of machine- learning algorithms. They minimize the dependency on the IT for data efficiencies and enable the business teams to perform self-reliant functions and democratize data preparation process.

Some issues that are commonly faced by enterprises concerning self-service data preparation technologies are as follows -

  1. Inconsistent data
  2. Conditional access to data
  3. Limited data integration facility
  4. Dependability on IT support

Listed below are the top self -service data preparation technologies which can improve the issues listed above, provide with efficient data preparation processes, integration with analytics, and self-service options.

1. Alteryx

Launched in 2010, Alteryx covers the four main functions of data blending, predicting analytics, dimensional analytics, producing and sharing insights. The insights provided by the software are relative and actionable since they are presented by utilizing the integral company information, cloud data and any related third-party data - configured in a single workflow. It excels in big data preparation and analytics.

Features

  • An increased working speed with blended drag and drop features of the workflow.
  • Provides relevant information only through up-to-date data cleansing.
  • Unlike other technologies which need complex calculations and processes to be relied upon, Alteryx offers predictive analytics feature which can be trusted in business decisions.
  • Predictive features provided by this technology can be customized, and the coding for the same can be imported from other sources.
  • User-friendly workflow, assisting in maintaining transparency with the help of reports creation and sharing features.
  • The latest upgrade - Alteryx 2018.2 offers centralized governance of data, data connectors, and analytics templates.
  • Features like customer churn analytics, assist in taking preventive and corrective actions.

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2. Paxata

Known best for being a self-service data preparation platform, it provides a seamless experience in data collection, exploration and analysis of even the most ad hoc and unstructured data. It is an innovative application and focuses on data quality, traceability, and governance.  Paxata caters to the dynamic requirements of business teams while offering real-time data solutions.

Features

  • Intuitive and highly interactive.
  • Data integration, data quality and analytics functions for business analysts.
  • The new Fall’18 Adaptive information platform which has an adaptive workload management capability and offers a dynamic scaling on large data preparation workloads.
  • Assisted intelligence feature to help interpret the data collected and prepared.
  • The visual user interface, not bound by operational constraints to make capacity meet demand.
  • Flexible deployment and self-service application.

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3. Datawatch

Provides a platform for visual analytics to acquire relevant data from both structured and unstructured resources. It also enables real-time data to be transformed into such analytics. Data can be extracted from the widest sources like PDFs or invoices and it can be viewed on real-time dashboards. Datawatch Monarch provides the most complex data in its simplest form for analytical use.

Features

  • Automation edition supports the automation of many data preparation tasks.
  • Visual workflow designs and analytics.
  • Datawatch report mining server aids in preparing and collecting data from data held in enterprise content management
  • The three monarch products support different scales of data. Monarch Personal supports few data sources,  Monarch Classic deals with moderate data sources and Monarch Complete supports big data with the aid of data connectors and salesforce.

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4. SSIS

SQL Server Integration services extract and transform data from a wide range of sources and can load the data in more than one destinations.  Being an enterprise level extract transform load (ETL) tool, it is a unique data preparation solution.

Features

  • Data can be extracted in various formats.
  • Easy configuration of data with the help of visual programming.
  • Data can be manipulated through the grant of ability to code for every user.
  • Suitable for moderate data input ingestion rates.
  • Gives access to view the step by step progress of the packages that are run.
  • Minimal training required to install and learn.
  • Multiple data transformation options available.

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5. Sisense

Sisense is currently leading in the agile business analytics market through its innovative technology, instantly accessible insights, and simplified analytical processes.  It effortlessly combines large sources of data from widespread resources for the desired insights, at any instant.

Features

  • No limitation as to the size of data.
  • Accumulates and cleanses data.
  • Data can be unified into visual dashboards.
  • Share data insights with colleagues and partners.
  • Instant deployment at a minimal cost.
  • Procure efficient analysis through visual reports without preparing them.
  • Sisense’s REST API can be used to integrate it with other applications easily.

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6. Trifacta

Primary features of Trifacta Wrangler software include data structuring, cleaning, enriching, validating and publishing. It is a cloud-based and on-premise software which can be downloaded to work locally on the data available. It is pivoted around the analysis of different data, interpreting and visualizing it. Trifacta works best with large datasets.

Features

  • Enforces data standardization.
  • Intuitive user interface and automatic profiling of data.
  • Central and robust governance, with elements for integration and security controls.
  • Guided experience for data wrangling processes.
  • Photon Compute Engine,  which is embedded into Trifacta application provides faster data processing and improved performance of the application. It can also visualize more volume of data.
  • Access to immediate help for all users through live chat support.

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7. Talend Desktop

   The most unique feature of Talend Desktop is that it is an open source tool which can comprehend data from almost any source. It simplifies previously scattered ETL services and works best on the enterprise level. Apart from self-service data preparation functions, it performs activity monitoring functions which assist in tracking tasks and processes.

Features

  • Big data can be cleaned and evaluated in a limited time frame.
  • Fully functional data preparation capabilities which can auto-discover the meaning of your data. It can highlight invalid values and provide solutions to correct them.
  • No data expertise is required.
  • Cleanses, enriches, merges and groups data across departments and enterprise.
  • Can be exported to external applications like Excel or Tableau for further analytics.
  • Completely automated sharing and collaboration features.

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8. Datameer

Data meer is a self-service data integration tool, for data from any source or variety and of any size of the volume. The primary functions of Datameer include - analytics, visualization, and integration and can be used across all departments of an enterprise. Datameer is an application app which scales with your needs.

Features

  • Iterative point and click analytics accompanied by drag and drop visualizations.
  • An open infrastructure and dynamic data management.
  • Analytic capabilities provided by Hadoop.
  • Single and spreadsheet-based interface- hence, comfort for excel users.
  • Simplifies SQL language in cloud era tools by converting it to a GUI.
  • Data can be imported through various protocols.
  • HTML 5 inclusion ensures the application is available on a wide range of devices.
  • Users can work individually or in groups.

 

CONCLUSION

Amidst these multiple choices available to users, user interface, governance, and limitations on profiling of data are the three central factors which should be considered before choosing the relevant data preparation tool which will meet your present and future needs altogether. Having certain evaluation criteria and standards will push you towards investing in the right technology. Self-service data preparation tools unlike the traditional data preparation tools, keep the analysts in control of the business for data preparation, access, and modeling. In the absence of these tools, this control would belong to the IT department, hence leading to delays, dependency, and inefficiency.

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