How to choose a data analytics platform? Core selection metrics and practical guides

in the wave of digital transformation, data has become the core asset driving business growth, and the data analytics platform is a key tool for mining the value of data. Faced with a wide range of data analytics platforms with different functions in the market, many companies and individuals are caught in the dilemma of selection, not only worried that the functions are insufficient to meet business needs, but also afraid that excessive investment will cause waste of resources. This article will start from the core selection indicators and combine practical scenarios to sort out a clear set of data analytics platform selection logic for everyone to help accurate decision-making.

How to match the functional requirements of the data analytics platform?

function adaptation is the core basis of selection, and only the data analytics platform that fits the business scenario can truly exert the value of data.

1, business scenario matching

different industries and different sizes of businesses have significant differences in demand for data analytics platforms. For example, retail companies focus more on user behavior analysis and sales forecasting functions, while manufacturing companies need to produce data monitoring and equipment early warning modules. When selecting a model, you need to sort out your core business processes, list high-frequency data analytics requirements, and then correspond to platform functions one by one. Preference is given to data analytics platforms that can cover more than 80% of core needs.

2, data processing capacity adaptation

need to pay attention to the data compatibility ability of the data analytics platform, whether it can connect with the internal ERP, CRM and other systems of the enterprise, as well as external third-party data sources; at the same time, it is necessary to consider the efficiency of data processing, in the face of large-scale data sets, whether it can quickly complete cleaning, conversion and calculation, to avoid card delay affecting the rhythm of business decision-making.

2. How to consider the data security of the data analytics platform?

data security is the bottom line that cannot be ignored. Once the data analytics platform has data leakage or permission confusion, it will bring irreversible losses to the enterprise.

1, data encryption and storage security

to check whether the data analytics platform adopts end-to-end encryption technology, covering the whole process of data transmission and storage; at the same time, understand the storage architecture of the platform, whether it has a remote disaster recovery backup mechanism, to prevent data loss due to hardware failure or natural disasters, to ensure the integrity and confidentiality of the core data of the enterprise.

2, authority classification management

perfect permission system is an important guarantee for the security of the data analytics platform. It is necessary to support different data access and operation permissions by post and department. For example, ordinary employees can only view reports of designated dimensions, data analysts can perform data modeling, and managers have global permissions to avoid the risk of data unauthorized access.

3. How to calculate the cost performance of the data analytics platform?

reasonable cost control is an important principle for enterprise selection, and it is necessary to comprehensively consider the short-term investment and long-term value of the data analytics platform.

1, explicit costing

explicit costs include licensing fees, service fees, deployment fees, etc. of the data analytics platform, it is necessary to clarify whether the charging model is based on the number of users, data volume or functional modules, and calculate the annual investment cost in combination with enterprise scale and demand to avoid subsequent hidden consumption.

2, hidden cost assessment

in addition to the explicit expenditure, but also consider the hidden costs, such as the learning cost of the data analytics platform, whether the operation is simple and easy to use, whether you need to invest a lot of time to train employees; and maintenance costs, whether you need a dedicated technical team for daily operation and maintenance, these hidden costs accumulated over time will also cause a lot of burden to the enterprise.

4. How to evaluate the landing ability of the data analytics platform?

excellent data analytics platform, if it cannot be used smoothly, it is only a castle in the air, and the landing ability directly determines the actual value of the platform.

1, deployment and implementation efficiency

to understand how the data analytics platform is deployed, whether it is a cloud SaaS model or a local deployment model, the cloud model has a short deployment cycle and no hardware investment, which is suitable for small and medium-sized enterprises; the local deployment model has higher security and is suitable for large enterprises with high data sensitivity. At the same time, it is necessary to confirm whether the supplier's implementation team can provide one-to-one guidance to shorten the platform launch cycle.

2, after-sales services and iterative capability

choose a data analytics platform, we should pay attention to the supplier's after-sales services system, whether there is 7 * 24 hours technical support, and whether it can respond quickly to problems; we should also check the iteration frequency of the platform, and whether it can optimize functions in time according to market demand and user feedback.

To sum up, the selection of data analytics platform needs to be based on multi-dimensional comprehensive considerations of functional adaptation, data security, cost-effectiveness, and landing ability. First, it is necessary to clarify its own business needs, and then correspond to core indicators one by one to avoid the misunderstanding of blindly following the trend. Only by finding a data analytics platform that fits its own development rhythm can we truly transform data into a driving force for business growth and help enterprises move forward steadily on the digital track.