In any interaction that you have with CIOs, CTOs, data science folks, analysts, and even others who just consume data, what you will hear from them is — when it comes to business data analysis, they are struggling because of the data preparation constraints (resources, time, and efforts).
Lack of quality data is the biggest factor that negates analytics efforts as we have observed from our meetings and interactions with both clients as well as participants in various community events such as the DataWorks Summit, Hadoop Summit, Sapphire, etc. That their respective analytics are not yielding the desired outcomes and, hence, not helping business decision-making, is quite a prevalent sentiment across industries and businesses.
Further, it wouldn’t suffice to just have quality data ready for analytics. The time consumed to get to that state of data-readiness is equally critical for measurable analytics success.
What’s the point if decision-makers get the required insights but not in time to have the desired impact on business functioning?
Delays in getting analytics-ready data occur owing to the extensive time it normally takes to prepare the data to be consumed by analytics engines/teams.
Let us look at what all happens before you get there:
Data users (directly or indirectly) need to connect with data-sources, bring in all the data (ingest) to a central location for analysis, organize the data, then cleanse it as not all data that comes in is consistent or standard enough for analytics, after that collect the required datasets and train them, undertake data mining to obtain the patterns, and if it’s a machine learning initiative then one would also need to refine the algorithms.
Obviously, a lot of the time is consumed in collecting, cleaning, and preparing the data for analysis because datasets come in various sizes and are of different nature and type.
Break-up of the time spent on the various data preparation activities points to the extensive effort required for data preparation.
- 60% – Organizing and cleaning the data
- 19% – Collecting datasets
- 9% – Mining the data to draw patterns
- 3% – Training the datasets
- 4% – Refining the algorithms
- 5% – Sundry tasks
That being said, the end-users –mostly people with basic computer skills and very little time to spend on non-focus, non-skill works — want visual interfaces that allow them to easily connect with any data-source, bring in any data they want to have a closer look at, and prepare and blend it – with absolutely no coding involved.
They would also need to collate all types of data (structured, semi-structured, unstructured) from databases and/or on-premises sources using standard queries, formulae, filters, and joins – all in a single solution. Otherwise, it’ll again be too much time spent in futility.
So how can you reduce the time from data preparation to data visualization?
You need a platform that can facilitate a host of data preparation activities to be undertaken without having to code.
An ideal solution for this would have the following features and capabilities:
- Provides an end-user friendly ETL (Extract, Transform, load) process:
- Business users have an easy and simple way to access and utilize data without depending on the IT department
- Users can simply drag and drop the required transformations and algorithmic components into the visual pipeline
- Connection with multiple data-sources:
- Allows users to connect to various data sources that are different in nature such as DBMS, NoSQL, Message Queues/Streams, etc.
- Provides easy access to the various types of datasets (structured and unstructured) coming in from databases and/or on-premises sources
- Facilitates data cleaning and modelling:
- Allows for users to easily and seamlessly ingest, prepare, and blend the data from all their sources with the help of a visually-appealing and intuitive user interface
- Users can, during the data preparation stage, also incorporate machine learning to the data to quickly obtain all types of data for analysis, thus leading to more relevant correlations and insights
- Allows end-users to use various transformation components like filters, joins, drop nulls, and fills etc., just by drag-dropping the required components (criteria)
As we can see, if a platform can offer all the above capabilities to users, then it would amount to something like what a developer/technical person can otherwise do in a full working day being accomplished in just a matter of 5 minutes or so.
That is like almost 100X faster! Wow!!
Thus, we see how enterprises can reduce the time it takes to prepare data for proper analysis and relevant correlations to obtain timely and actionable insights.
MSRCosmos’ HCube Data Studio comes with all the analytics-accelerating features mentioned above.
Register, for a 30-day free-trial of HCube with your data, here https://hcube.msrcosmos.com/TrialVersion