Enabling Analytics with Pro Data Management Tips
If you’ve ever tried and failed at facilitating analytics efficiently, without the risk of using inaccurate or obsolete data, this article is for you. We will address effective data management tips to guarantee accurate analytics in business decision-making and BI solutioning. Data management encompasses all the processes for proactively accumulating, unifying, protecting, storing, and sharing data.
If you understand the importance of data management for decision-making, then chances are high that your company already has some tools and processes for handling your data. But how well do these tools and processes work together for your enterprise? And are you sure that your organization’s data is being used as efficiently as possible? Do your data management activities align with your analytics requirements? If you find yourself stumped by these questions, you & your organization might benefit from our strategic data management pro tips. With enterprises accelerating data analytics practices, data management is imperative to transformation.
Having accurate data helps organizations solve business problems—especially in rapidly shifting markets. Here we present the power of data management and explain how successful data management can encourage your analytics initiatives, as a strong foundation for reliable analytics techniques, effective integration, and business growth.
Challenges Faced by Enterprises
We all know data is the key to transformation but many organizations still struggle to achieve data & analytics at scale—and data management is the most foundational challenge to overcome. An often-ignored truth is that before you can do really exciting things with analytics, you need to “ensure” reliable data management first. If data management is practiced right, your company can easily overcome some of the following common data challenges:
> Data management processes not integrated into workflows make data and analytics more challenging to scale.
> Disconnected data silos and legacy tools make it hard for people to find and access the data they need for making dependable decisions.
> Data activities that consume time and resources but do not contribute to overall business operations objectives.
> The ever-present struggle to find the right data when it’s needed most.
Successful Data Management tips to boost your analytical experience.
“Untainted” data is core to a profitable business – specifically one that banks on analytics to survive. Preparing your data for analytics according to your business can be full of challenges. Follow our data management pro tips to transform and shape your data for any analytic purposes. As a result, you can gain deeper insights, install that data into models, develop unique strategies and automate decision-making processes across your enterprise.
Tip #1 : One Size Does Not Fit All
It’s crucial to fully satisfy data management requirements to lead a successful analytics approach or solution. Data experts or business leaders who ignore the requirements restrict their solutions and majorly risk failures. Hence, the key is to remember that every approach to analytics has discrete requirements for data management that need to be addressed.
Tip #2 : Make Analytics Talk to the Right Data
Ensure that your analytics connects to data from the “most” appropriate sources. For effective analytics, the data must encompass:
> Rich information about business entities of interest.
> Best schema for the tools used.
> An acceptable level of quality.
> Delivered at the right time.
Modeling your data with the right relationships and cardinality is key in this step. Determine schemas that best represent your business rules, entity relationships, and analytics infrastructure. For example, algorithms for data mining, statistics, and machine learning work well with inconsistently structured data of poor quality that too with no metadata. Whereas, data warehouse analytics based on hierarchies, time series, and dimensions needs ruthlessly structured, cleaned, and documented data. Hence, data management differs according to analytics infrastructure.
Tip #3 : Data Management based on your Unique User Expectations
Understand your end-users frequency of data querying, performance expectations, data availability expectations, and ensure you plan your data management to fulfill these expectations. Identify the query angles and analytics persona of the people involved because their usage of the setup will define the success of your effort.
Tip #4 : Data Governance is a Journey
Data governance is recognized as a foundational element of any strong data management design. As a critical enabler data governance helps organizations maximize the value of their analytics and data assets. Good data governance is an ongoing journey than a destination. So prepare to iteratively and periodically assess, identify and improve wherever possible.
Tip #5 : DataOps
Unlock the power of analytics with DataOps. When companies construct data infrastructure, they often miss forming scalable architectures. Based on the principles of DevOps, DataOps employs agile development to data analytics and business operations. To deliver the data quickly and efficiently, DataOps combines technologies, processes, and protocols to streamline the end-to-end delivery of data to stakeholders within a business.
Plan for an Uncertain Future | Compete on Analytics | Grow Customer Accounts
These high-value business goals entail advanced forms of analytics, which in turn demand appropriate data management, data integration, data platforms. Without the right data in the right format and strong data management, critical efforts in advanced analytics have little or maybe no business value.
No matter what kind of business you run, data management is indispensable for surviving in this competitive market. Many organizations realized that though transforming into an insights-driven business takes months, proper data management culture is essential. Hence, your company should have a comprehensive data management and analytics strategy in place for strategic decision-making.
- Business Intelligence
- Data and Analytics
- Self Service BI
- Strategic BI