In the present day, many businesses are intertwining data management with their overall corporate strategy, to form a catalyst for sustainable growth. Strategic utilization of data offers significant benefits when compared to data management alone.
According to the IT research firm Gartner, the role of chief data officer will become as vital to overall success as effective IT, HR, and finance management for 75% of large businesses by 2021. This represents a fast-growing need for comprehensive data management strategy in the enterprise, to maintain qualitative parity of service delivery in the face of growing customer demands.
An effective data strategy requires you to support data management by maintaining and improving data quality, integrity, and accessibility. At the same time, constant re-evaluation of your data security will mitigate any potential risks associated with the processing of sensitive customer information. You should compile these processes into a framework, which you can then communicate with employees to promote adherence to your data policies.
Let’s analyze what constitutes an effective enterprise data strategy, from the top-down.
Most businesses will approach data strategy in one of two ways:
Bottom-Up - Your business may have massive quantities of data available, but no predetermined objectives for using that data to extract insight. This method involves working from the ground up, uncovering data points to reveal insights that can then influence your overall corporate strategy.
In a big data environment, this can be difficult to manage as massive quantities of data are collected without considering the purpose. This results in masses of unstructured data that may end up being predominantly useless for business intelligence purposes. Without careful management, this will increase storage costs and the complexity of data security compliance. There is still value to this approach, albeit with more demanding processing requirements.
Top-Down – A top-down approach is typically used during the implementation of new systems. Your business defines objectives for the new software, so you can pick and choose only the relevant data needed to gain insight and achieve your goals.
This method allows you to forecast business requirements, limiting the amounts of data you collect. You can filter information, structuring your data upon input to your databases to streamline administration processes. By doing this, you lower your storage requirements and facilitate a proactive approach to data security management. Sensitive information can be redirected to more watertight storage destinations in your infrastructure, minimizing risks. Overall, the data you collect is more useful from the get-go, but you miss out on hidden information contained within the raw data you omit.
Numerous tools can be integrated into your enterprise data strategy. These include:
Master Data Management – The term “master data” refers to your data source, of which analyses are derived from. Also called “the source of the truth,” this is a vital dataset as it determines the validity of your insights. A master data management tool will streamline governance, ensuring the integrity and validity of your datasets.
Data Warehousing – This type of tool will govern the process of collecting and managing your data, offering integration between raw heterogeneous data sources to facilitate insight through the use of business intelligence tools.
Business Intelligence And Data Analytics – These tools expedite decision-making processes by elucidating the changes needed for operational improvement. This is achieved through trend analysis, creating competitive advantages through the understanding and forecasting of customer and business requirements.
Trianz is a leading enterprise data strategy consulting firm, partnered with numerous industry-leading data management vendors. We specialize in master data management, data warehousing, and business intelligence, developing unified solutions to maximize data value for your business.
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