Big data is a big business. Especially now when it’s increasingly being used to analyse human behaviour and reveal patterns. Companies can use this information to gain insight into their target market. While American Express uses big data to forecast customer loyalty and potential churn accurately, Starbucks thrives by analysing the information related to location, traffic, area demographics and customer behaviour. With machine learning and artificial intelligence coming into play, companies can get clues from big data and perform at the optimal level.
A huge amount of data is being created through server applications, software and hardware components. However, the data is generated at the edge of your network and cannot be easily accessed in the hour of need. Consequently, many companies are still struggling to process information in real-time.
The fragmented IT infrastructure of smaller companies further adds to the complexity. Traditional IT infrastructure lacks the scalability and agility to anticipate business needs and deliver data as a service. So, why not re-design the IT infrastructure and make it data centric? An IT architecture with data at its heart should be created on the basis of these five key principles.
1. Define Your Data Goals
You have data coming in from all departments, be it manually or automatically. To determine the data that needs to be processed further, you should assign certain goals and objectives. If your goal is to achieve increased sales in a given period, you need to set metrics for your data to qualify for the same. This way, you will not end up drowning in the sea of data. Your sales team can identify the importance of different datasets, and utilize them to achieve feasible goals.
2. Collect Data Produced Internally
The management of data collected from multiple touch points has become a major challenge for organizations and their IT professionals. To achieve the full potential of data, it’s time to consolidate and bringing together numerous applications into one simplified pool. All-flash can be used for converging the tiers of data storage and achieve higher agility and security. All-flash and hybrid storage products are backed by solid-state memory technology to ensure low latency and greater storage density.
3. Extract & Analyse the Value-Added Data
Today, most of the data is processed through algorithms, without any human intervention. As a result, it becomes difficult to extract information in real-time, as your business may require. The human input is important to generate value out of the processed information. Let your team members and corporate leaders determine in advance the type of data they want to extract at different stages. They should anticipate their business needs and accordingly design scalable storage services and APIs (application programming interface). This process will lower down the incidences of reactive troubleshooting, and data as a service can be delivered as soon as there is any requirement.
4. Organize the Data in Multi-Cloud Architecture
At present, most of the data collected by organizations are unstructured. Though it’s presently impossible to collect 100 percent of the data, you can still manage the chaos through multi-cloud capability. With the multi-cloud architecture gaining popularity, it’s now easy to deliver the cloud data experience to multiple development environments. You can manage data across multiple clouds with higher portability and openness. At the same time, you can deliver unique capabilities to each IT environment, be it your production cloud, cloud for analytics or multiple development setup.
5. Encourage Collaboration
Extracting and analysing the quality data is a team effort. Your IT team and other departments must come together to decide what’s critical for making informed business decisions. For instance, your sales and marketing team can come up with metrics to gather and qualify data, while your IT team can assess the possibility for the same. The collaborative effort will help in leveraging the power of big data without interrupting the workflow.
A data-centric architecture is critical to simplify core applications and empower employees with on-demand information. You can create a data hub with continuous integration and delivery, and promote informed business decisions.