The document discusses trends in big data and business intelligence (BI) for 2017. It predicts that data volume and variety will continue to grow rapidly, driving increased adoption of tools like Hadoop. However, speed will become more important, leading to faster databases and optimized query engines. Integration of IoT, cloud, and big data is also expected to increase, as is the use of big data to enhance customer experience. Self-service analytics platforms will grow in popularity to make big data more accessible, and deep learning will be applied more to analyze unstructured data. Metadata catalogs and data virtualization will help users discover and access data. Architectures will mature to support both batch and interactive analysis. Governments may increase scrutiny of data handling

2. WHAT IS
BI
TODAY?

3. All that was ‘hype’
related to the term big
data years ago
became a justified
norm in the world of
business this year.

4. Data has become the new
currency and the
technologies now revolve
around the concept of
putting the big data into work
and increase ROI through
enhanced
productivity and
minimal risk.

5. BI in the year 2017
The year 2017 will continue to witness massive growth of data both in terms of volume
and variety. With this kind of growth, we will see a simultaneous rise of systems
catering to processing data in more real-time.

6. Here are
the most possible
trends around...

7. Speed up Hadoop
This year will witness a surge of organizations who
will be willing to adopt big data stuff, Hadoop, and
myriad Hadoop Solutions.

8. With Hadoop, organizations of any size will be able to process
large volume and variety of data using advanced analytics to dig
valuable information and use the same to make profitable
decisions.
Speed up Hadoop

9. However, speed has become an integral part of
everything nowadays so adoption of faster databases like
MemSQL, Exasol and other Hadoop-based stores Kudu
had become imperative.
Speed up Hadoop

10. Also implementing OLAP on Hadoop technologies like
AtScale, Jethro Data and SQL on Hadoop engines like
Apache Impala, phoenix, drill accelerates queries and
keeps to the pace.
Speed up Hadoop

11. Convergence of IoT, cloud and Big Data
IoT generates large volume and
variety of data and a huge
portion of this data is deployed
on the Cloud.

12. Convergence of IoT, cloud and Big Data
The data will reside in myriad
relational and nonrelational systems
that include Hadoop clusters to
NoSQL databases.

13. Convergence of IoT, cloud and Big Data
So capturing data and analyzing the same
from innumerable sources itself will be a
pretty tough challenge and demand for
analytical tools that have the capacity to
seamlessly connect and combine a variety of
cloud-hosted data sources will definitely
increase.

14. Convergence of IoT, cloud and Big Data
And such tools will aid the
businesses to explore and figure out
the hidden opportunities in the data
gathered.

15. Using Big Data to enhance CX
This year will focus on
enhancing CX with the help
of big data, by moving its way
from legacy to the vendor
systems with hard core
system upgrades.

16. Using Big Data to enhance CX
It aims at doing so by
analyzing data with self-
service flexibility and
deriving insights about the
ongoing trends.

17. Using Big Data to enhance CX
People would be using the big
data analysis to interpret the
behavior of the customers and
thereby enhancing customer
experience and increasing the
revenue by reducing the
churn.

18. Self-service analytics platform
Self-service data prep will be mainstream in the
upcoming year. The end users play a major role in
shaping the big data.

19. Self-service analytics platform
The biggest challenge facing the world of big data is to make
the Hadoop data accessible to the business users. However, a
step towards achieving this goal has already been taken in
the form of self-service analytics platform.

20. Self-service analytics platform
Agile self -service data preparation tools not only helps in data
prep at the source but at the same time makes the data accessible
in the form of snapshots for quick and lucid exploration. These
tools are minimizing the barrier for late Hadoop entry and will
gain traction in 2017.

21. Deep Learning
Deep learning revolves around
machine learning based on neural
networking and imbibes great
potential in solving business
problems.
It aids the computer to identify the
items of interest in unstructured data
of massive volumes to deduce
relationships without specific
programming instructions.

22. Deep Learning
The algorithms cater to the
domain of artificial
intelligence mostly, which
has the ability to observe
the patterns and gauge and
make decisions for complex
problems.

23. Deep Learning
So, deep learning is mostly helpful
for learning from massive volumes
of structured and unstructured data
and extracting meaning from them
and patterns from big data.
Organizations are bound to pay
more attention to unsupervised
training algos to take care of the
heavy influx of the data.

24. Data Warehouse is heating up in the cloud
The death of data warehouse has been quite the talk in the Big
Data world for some time now!
The pace obviously has declined but we have been witnessing a
major shift in patterns in this technology where Amazon is
now leading with the concept of on-demand cloud data
warehouse.

25. Data Warehouse is heating up in the cloud
According to analysts, 90% of companies who have already
adopted Hadoop will be sticking on to their data warehouses
and with the new upcoming, the customers can scale the
computing resources and storage accordingly in data
warehouse compared to the huge volume of information
stored in the Hadoop data lake.

26. Rise of Metadata Catalog
The concept of Metadata catalog aids
the users to discover and explore the
relevance of data.
They make use of tags to understand the
relationships between data assets and
also provides query suggestions, thereby
minimizing the time to get hold of the
accurate data.

27. Data Virtualization
This year will witness a strong
magnetism towards data
virtualization.
Data virtualization unlocks the
hidden concepts and conclusions
from a large set of data.

28. Data Virtualization
Graphical data virtualization
allows the enterprises and
organizations to retrieve and
manipulate data on the go, no
matter where the data is residing
and in which format.

29. Architecture Matures
Hadoop is just no more a
batch processing
platform but has
upgraded itself to be a
multi-purpose engine for
ad-hoc analysis.

30. Architecture Matures
It has started to been used to for
operational reporting on day to day
basis similar to the way done by
traditional data warehouses.
This year, enterprises will cater to
these hybrid needs by following
use case specific architecture
design.

31. Architecture Matures
The modern architecture would be
mostly needs to be driven, and they
will look forward to combining the
data-prep tools like Hadoop Core
and end user analytics platforms so
that they can be configured and
reconfigured as per the evolution of
the needs.

32. Government Scrutiny
We can expect a lot of government interference in the way data is going to be
handled in this year.
Government scrutiny will be done on each and every data that are being used by the
companies and various government departments.

33. Government Scrutiny
With the ever increasing variety and volume of data, there has been a constant rise
in the rate of cyber attacks, so governments will have a hand in this big data concept
in this year. Now all that we are waiting for is how this will be done and how it will
impact us in the coming year.

34. As said earlier that data has become the new currency and with the ever
increasing pace of growing connected devices gargantuan volume and variety of
data is generated.
So big data is bound to play an extremely vital role this year and at the same
time help the organizations to derive valuable insights that would shoot up their
business to the new level of success.
CONCLUSION

35. Official Blog Link - http://www.algoworks.com/blog/10-top-notch-big-
data-trends-to-watch-out-this-year/
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