Six steps for businesses to become data driven Six steps for businesses to become data driven
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Six steps for businesses to become data-driven

Six steps for businesses to become data-driven

It is essential to eliminate the instilled idea that data has a place with only one specified group


In the modern business world, there’s no doubt that having a data-driven approach is essential for accelerating growth. However, becoming data-driven is not something that happens overnight. It is a journey that encompasses many different aspects of the business at a functional, cultural, and operational level.

The following six steps are designed to help enterprises looking to become data-driven set themselves up for success:

1. Cultural changes
Lines of business (LOBs) have become ever more ambitious about what they want to do with data. Often they want to achieve complex outcomes in a very short time frame. This makes it extremely difficult for businesses to depend on old-school methods of LOB groups requesting that IT furnish them with the data they need. Instead, where we see data-driven cultures thriving, is when LOBs and IT collaborate closely on business objectives, data, and processes. To be a good partner to LOB, IT needs to understand new tools and available methods in the market that make it conceivable to disperse data from the executives and administration to the groups while maintaining authority over the base framework. Moreover, it is essential to eliminate the instilled idea that data has a place with only one specified group. Data of an organisation belongs to every party in the organisation. The key to organisational success is through unison.

2. Involve data security and accountability teams from the beginning
While the most successful businesses democratise access to data, it is important to maintain confidentiality and the secrecy of sensitive data. This means that from the very initial stage of shifting to a data-driven culture, it is crucial to propagandise data security and liability. The organisation’s data platforms should simplify reading datasets, improving how users differentiate between types of data to determine what is sensitive and shouldn’t be shared and what must be shared to support business objectives. Being able to identify where vulnerable data is placed, who it is accessible to, and how this accessibility can be controlled to assure that only a specific set of individuals have access to this data, from a particular location is important. The data platform should also observe data (access) history and any conversions or manipulations throughout the data lifecycle.

3. Adopt cloud-native and public cloud data frameworks
In order to create an agile data structure, it would help to usefully regulate, on-demand public cloud abilities. Capacities, value, and global accessibility are distinctive from one public cloud to another, having a “multi-cloud” approach hence empowering developers to identify and utilise the best available cloud platform for every individual dataset. This then prompts better compatibility, which therefore creates an improved performance, reduced costs, and motivates innovation. In that capacity, this eliminates the need for developers to create shadow IT to address their application challenges.

4. Transform local data servers into a private cloud
In spite of the attractiveness of the public cloud, businesses tend to depend on local data servers, maintaining the same approach for decades. To make the shift into becoming a data-driven organisation, it is crucial to improve the way insights are managed or acquired from the local data centre. The idea is to convert the monolithic deployments of the packed compute and cache, into a private cloud with all the adaptability and readiness that the public cloud gives, including all the required commands.

5. Connect both cloud types to create a hybrid model
Connecting the local framework to the private cloud, creates an ideal opportunity to attach, therefore access, the private cloud with various public clouds – your Hybrid Data Model. This model makes it easier to supervise the data on the go, giving real-time interpretations effortlessly. Moreover, this model provides agility to automate how assignments and data move around across any desired platform with better cost, privacy, and productivity.

6. The technology matters
The journey doesn’t just end solely by installing the hybrid data capacity. It is essential to attach the right tools for real-time insight. The chosen tools must be compatible with all interpretations and data types, enabling users to efficiently take advantage of the integrated services to meet individual requirements. Lastly, the key aim here is to get tools that make it possible to automate! This is the only way employees can fully gain the upper hand of all the data provided at their fingertips.

The initial process of turning into a data-driven business might seem to move at a snail’s pace, however, it is definitely worth the effort. With the current competitive business environment, having a data-driven culture truly pays off. It is hence crucial to find colleagues who understand the challenges encountered by large-scale businesses coping with data overload to assist the business during the transition.

Ahmad Shakora is the regional vice president – Middle East, Turkey and Africa at Cloudera

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