Managing infrastructure is hard, and if it is a mixed infrastructure like Big data on Cloud, you need to think bigger. With the enormous growth and popularity of big data, this cutting-edge technology has helped evolve lots of new tools. Besides that, it has enhanced the technical complexities like difficulties in on-premises installations. Thus it introduces the power of Cloud technology, enabling the level of agility that allows data strategy and cloud strategy to intersect with each other.

However, whether cloud strategy or data strategy, which one is more important to get the best business outcome? Hence, in this blog, we will discuss the different aspects of data strategy and cloud strategy to assess the two’s importance.

Why do you need data strategy?

Data Strategy is an actionable foundation and a comprehensive vision for an organization. It helps to define a plan for how an organization will implement better outcomes by harnessing their data. Furthermore, the data strategy is an umbrella term representing all derived domain-specific strategies like Business Intelligence, Master Data Management, Big Data, and many more.

The key features of an enterprise data strategy are:

– Actionable

– Evolutionary

– Relevant

– Connected  or Integrated

Every organization needs one data strategy for the below-mentioned reasons:

– To avoid data duplication, an enterprise requires a centralized vision for its data-related capabilities. This will also help to prevent any unexpected drive-up of future costs.

– The Data Strategy works as a key factor for all enterprise planning efforts related to data-related capability.

– The Data Strategy helps in the unification of IT and business expectations regarding all enterprise data-related capabilities. A detailed and comprehensive data strategy helps to understand both the streams resulting in better business outcomes entirely.

– A data strategy is the best possible measure to define the service level expectations applicable across the enterprise.

Through data strategy, it can be explained effectively how the management of enterprise data could be leveraged to support an organizational mission.

– With a data strategy for data analytics, an enterprise can react proactively to prevent poor performance. It can make necessary changes effectively to turn a negative situation into a positive one.

Related post – How Big data and Cloud computing complement each other

What is data analysis strategy must include in its list?

Though data strategy for analytics does not need to be very complex and involves a large data analytics team of data scientists, however, it must follow some standards as follows:

– It must be aligned with a pre-defined list of business goals

– It should identify an effective approach for data exploration within the organization

– The strategy should include data analytics tools for data exploration, data visualization, and analytic modeling

– The strategy must lead to a path of machine learning and predictive analytics

Why do you need a Cloud Strategy and what are Cloud strategy advantages?

A Cloud strategy is one that:

– Identifies key business trends using platform-as-a-service tools for data-based analytics

– It helps to assess positive and negative business outcomes and also provide suggestion on how to achieve or avoid them

– It drives actionable outcomes based on data analysis decisions

To build a proper cloud strategy, an organization does not always need to migrate fully. Instead, it can adopt a hybrid cloud solution, an excellent approach to include cloud scalability and agility into the overall cloud strategy. A good Cloud strategy provides the benefit of increased scalability while decreasing the cost. Moreover, a hybrid cloud strategy helps fill the existing environment gaps rather than sweeping the technological changes across the organization.

What are the key considerations for a cloud strategy?

A cloud strategy must include the following:

– To identify the tools and capabilities that an organization is lacking which the cloud vendor can provide as the substitute.

– To identify and describe the data sources and how they will be stored on a cloud platform. If it is a Hybrid cloud platform, then the strategy must clearly define which data can live in the cloud and which one needs to remain on-premises.

– Ideally, the strategy must focus towards platform-as-a-service offerings to avoid the administrative and maintenance overhead.

– Identifying the tools that the organization does not possess but the inclusion of which can provide the capabilities of the organization.

– Strong integration between existing infrastructure and proposed infrastructure to build support for both corporate and departmental deployments. It must be flexible and dynamic as well based on business needs.

– It must consider the individual shortcomings or features of a particular cloud vendor to avoid future issues. For example, as Microsoft Azure is secured by design, though including data security is a key priority, overarching security clauses may hinder the overall progress.

– To identify the key point of contact as an owner for a specific cloud-based tool. As the feature updates happen quickly in the cloud, hence, having a single point-of-contact can help in the quick progress of adoption.

Why data strategy and Cloud strategy are compared though they are completely different?

Big data and Cloud computing fit well with each other which is the main reason that data strategy intersects with cloud strategy considering many factors.

Some of the reasons are as follows:

– Many Big data analytics tools like Hadoop are complex, with its large ecosystem and community-built packages. Not to mention, a large ecosystem creates a constant administration burden to an organization. On the contrary, going to a cloud platform minimizes such administrative overheads.

– With the increased use of analytics, it is going through constant upgradation, which brings more matured analytic models. Consequently, the tools mature with the models. Updating with such new features is easier in the Cloud platform than on-premises architectures.

– The cloud platform is agile which supports the ongoing changes in analytics models with the change of business requirements.

– With a Cloud solution, you can easily scale up your big data solution with data growth. The primary reason for that is Cloud has the hyper-scalability. Hence, it is easier for budgeting and as well as cost-effective.

– Data analysis always deals with cutting-edge trends, while the Cloud platform also follows the cutting edge. Hence, data analysts on the Cloud platform always work with the latest technology.

– Cloud platforms leverage the facilities to integrate the development cycles and deployment cycles by building them into the cloud platform. This follows an agile pattern. Hence, the business analysis team can easily manage their own release schedule and get the right answers ready for the business stakeholders.

What is your data strategy that matters a lot than Cloud strategy

In big data –Cloud combination, Cloud is a commodity to work with, whereas data is the data analysis’s key driver. From the above discussion on data strategy and Cloud strategy, we can conclude that when an organization is enabling big data on Cloud, the primary concern should the data strategy. In a nutshell, the key considerations here are:

How to manage data seamlessly across mixed infrastructure?

In this scenario, a detailed analysis of big data analytics’s on-premises infrastructure must be evaluated against the particular Cloud infrastructure. This can explain what big data-related services the vendor can provide, and if any migration is required, how that can affect the overall planning.

What would be security and data governance policies in a cloud infrastructure?

Based on the Cloud infrastructure, the security and governance policy must be defined, and it should be in a way so that it cannot hamper the process flow.

The ability of the organization to work with the particular Cloud vendor in case any change in data

Ideally, the Cloud vendor must be able to provide the necessary support to the big data system in case of any change in data.

How to avoid sole dependency on a particular Cloud vendor?

Data strategy should be in a manner that it should not depend on a particular Cloud vendor service. So, if the organization needs migration, it will not affect technically or concerning cost measurement.