Incorporating DevOps in Business Intelligence
DevOps started off as a methodology that integrates Developers and Operations teams to work in tandem in software development projects.
DevOps started off as a methodology that integrates Developers and Operations teams to work in tandem in software development projects. It facilitates seamless coordination and communication between teams, reduces time from idea to market and significantly improves operational efficiencies while optimizing costs. Today, DevOps has rapidly evolved to include several other entities of IT systems. A new addition is Business intelligence. DevOps jelled well with Big Data as both methodologies are contemporary and complement each other in managing of massive volumes of live data moving between development and production that is maintained relevant via seamless coordination between teams. When it comes to business intelligence, data warehousing and analytics are two important components that need to be managed. As BI deals with batches of data, it doesn’t easily integrate with the DevOps environment by default.
Managing
Data Warehousing with DevOps
A
data warehouse is a central data repository that collects data from various
disparate data sources in and outside the organization and hosts them in a
central location allowing authorized people and reporting and analytics tools
to access it from any location. Managing a robust and sophisticated data
warehouse is a challenge as multiple stakeholders are involved in making a
change which makes deployments rather slow and time-consuming. Implementing
DevOps here can be a revolutionary thing as you can combine data administration
teams and data engineering teams to collaborate on data projects. While a data
engineer informs potential features that are being introduced to the system,
the data administrator can envisage production challenges and make changes
accordingly. With cross-functional teams and automated testing in place,
production issues can be eliminated. Together, they can build a powerful
automation pipeline that comprises data source analysis, testing,
documentation, deployment etc. However, introducing DevOps for data warehouse
management is not a cakewalk. For instance, you cannot simply backup data and
revert to the backup as and when required. When you revert to a last week’s
backup, what about the changes made to the data by several applications?
DevOps
for Analytics
The
analytics industry is going through a transformation as well. Contrary to the
traditional analytics environment that uses a single business intelligence
solution for all IT needs; modern businesses implement multiple BI tools for
different analytical purposes. The complexity is that all these BI tools share
data between them and there is no central management of BI tools. Another issue
is that data scientists design models and algorithms for specific data sets to
gain deeper insights and offer predictions. However, when these data sets are
deployed to the production environment, they serve a temporary purpose. As data
sets outgrow, they become irrelevant which means continuous monitoring and
improvement is required. The rate at which the data drifting happens is
enormous and traditional analytics solutions are inefficient to manage this
speed and diversity. This is where DevOps comes to the rescue. DevOps helps
businesses integrate data flow designs and operations to automate and monitor
data enabling them to deliver better applications faster. Automation enables
organizations to build high performing and reliable build-deploy iterative data
pipelines for improving data quality, accelerate delivery and reduce labor and
operational costs. Monitoring data for health, speed and consumption-ready status
enable organizations to reduce blindness and eliminate performance issues. It
means a reliable feedback loop is created that covers data health, privacy and
data delivery for ensuring smooth flow of operations for planned as well as
unexpected changes.
If
the data acquired in an organizational structure is not organized, it is hard
to make any sense out of it. The success of the data intelligence always aids
in the effectiveness and organizational efficiency. Adopting DevOps principles
while implementing BI methods will help in improving data quality and
inter-team communication. If the data is new, important, and uncorrupted, the
outcomes obtained from it will be beneficial. However, defining ‘new’ and
‘important’ is a challenge indeed when dealing with various data streams that
are growing continuously. Traditionally, BI applications process the data they
collect in large quantities, often leading to blunders. This points out the
necessity of adopting a DevOps approach to data mining. This helps to automate
the testing process making it more accurate.
Analyzing the data in this way helps you bypass errors and
misconceptions that will block your progress later on. Enterprises who have
chosen a DevOps strategy to BI claim to have a deeper immeasurable situational
consciousness than ever before. As most company owners comprehend, possessing
all of the technology in the universe won’t replace the vitality of
communication within your team and across team units. If the team members are
not acting together to interpret and utilize the data that’s accessible to
them, it will obstruct your organization from reaping the rewards of business
intelligence. One of the fundamental policies of the DevOps process is
collaboration. Making sure your team is steadily in communication about what
they are finding in the data you have collected is essential to get the maximum
out of business intelligence both short-term and beyond. Business intelligence
is not just limited to data warehouses (DW) and ETL (Extract Transform Load).
It also encompasses services between the ETL processes as well as the
middleware and dashboard visualizations. Communication and negotiating
agreements among these layers is complicated and demands much efficient
coordination. DevOps benefits facilitate this with repeated deployments and
testing. The DevOps approach is a part of the agile methodology that encourages
continuous iteration of development and testing throughout the software
development lifecycle of the scheme. Crashes aren’t only presumed, but
encouraged. By implementing the identical policies to Business Intelligence
deployment, businesses can customize their solutions to an exceptional degree.
“Traditional IT has always feared change, which is the main root cause for most
of the operational issues. A way to minimize change was to slow down the
delivery processes with numerous review, assessment, and approval workflows.
However, today change is not only inevitable but necessary in order to deliver
the speed and agility expected from IT by business… DevOps is frequently viewed
as a synonym to speed but like in racing, higher speed should come with greater
safety.” – Sasha Gilenson, CEO of Evolven DevOps intersects over organizational
hierarchy, demanding everybody, from the administration to the front end
developers and testers, to adopt failures as long as the successive step is an
elevation. This strategy moves BI users closer to the ‘authenticity’ of their
profession and promotes shaping the BI solution as an annex of their
organizational ‘intuition’. The DevOps procedure accelerates Data Warehouse
(DW) management by drawing all stakeholders to the table and making them
accountable. Their role is no longer to simply give consent, but be available
for further feedback and advice until the solution is ‘deployable’. Prompt
feedback helps keep the solution consistent and productive. DevOps enhance the
situational awareness of business owners, enabling them to make more
knowledgeable judgments.
Bringing
DevOps into the BI realm is not an easy task as BI environments are not
suitably designed for DevOps. However, businesses are now exploring this
option. Bringing DevOps into the BI segment gives situational awareness to
businesses as they can make informed decisions when they gain insights into relevant
data added from multiple sources. Moreover, it brings great collaboration
between teams, allows better integration between different application layers
while helping businesses to explore and quickly tap into new markets. Most
importantly, it makes your business future-proof.
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