Turning big data into high-class insights

Turning big data into high-class insights

Turning big data into high-class insights

Nick Ismail
To compete effectively, large organisations need to extract actionable insights from all their data faster and more accurately than ever.
However, they are usually hampered by complex IT estates which make the untangling of this data spaghetti expensive and difficult, typically the data is taken from 20 to 100 different platforms.
The result? Datasets fail to reconcile, new data feeds take months to establish and different departments duplicate one another’s work (inconsistently).
So confidence in the quality of data is low, hindering business action from insights.
Analysts entering a data warehouse may find as many as 5 or 6 different versions of a metric they are seeking, since the analyst does not know which version to trust or is most suitable, he creates yet another metric, so typically 80% of his time is eaten up wrangling data – making it usable.
>See also:  Top 8 trends for big data in 2016
A massive source of duplication and a waste of valuable time that should have been spent producing actionable insights.
An organisation operating in this environment has no single source of the truth, leading to mistrust as departments build their own data marts in order to get on with their own work.
At the same time, projects frequently over-run both in terms of cost and time, stacking up overheads.
Metadata is the key
Now, a new approach is enabling businesses to save millions of pounds by integrating data faster, better and more cheaply than ever, irrespective of technology.
This is the metadata driven estate (MDE), generating and authenticating insights through the use of metadata in a managed hub that spans all platforms.
In simple terms this means that MDE unpicks the complexity of the “spaghetti” to produce data lineage showing exactly where a metric has come from and how it is calculated, whether it is in any sense polluted and who is using it.
MDE gives the analyst an easy to use search box to find all available metrics appropriate to his subject, the lineage and usage of each so that he easily decides which to choose.
MDEs automation ensures all the data is quality-managed tracking DQ issues on each data refresh, this improves confidence among analysts and end-users, across all data management platforms including Hadoop.

Read Also:
How predictive analytics discovers a data breach before it happens

 



Chief Analytics Officer Europe

25
Apr
2017
Chief Analytics Officer Europe

15% off with code 7WDCAO17

Read Also:
Get the Most from your Voice of Customer Data

Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
Detecting shipping errors with the shippers’ own data

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
Why Embedded Analytics Will Change Everything

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
Detecting shipping errors with the shippers’ own data

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Data Governance Leads to Data Quality (not the other way around)

Leave a Reply

Your email address will not be published. Required fields are marked *