Embracing the right data management principles can really help companies identify the reference points they need when approaching big data. We identified 5 guiding principles of data management that apply to all types of business, big or small, B2C or B2B:
Moving towards a more data-driven culture is certainly attainable, and it starts with a Data Strategy. A Data Strategy is often viewed as a technical exercise, but a modern and comprehensive Data Strategy addresses more than the data; it is a roadmap that defines People, Process, and Technology.
In an enterprise setting, the term ‘ownership’ generally assigns a level of accountability and responsibility for specific datasets. This is possible by processing and aggregating customer data for commercial exploitation by creating new products and services, enhance its internal operations, products or services, or licensing the data to third parties
Metadata specifies the relevant information about the data which helps in identifying the nature and feature of the data. It makes the wider data management processes intelligent and dynamic. Active metadata can be the vital foundation of a well-architected data management system, yielding benefits across the entire lifecycle of data projects.
Data quality is crucial – it assesses whether information can serve its purpose in a particular context. It comprises of maintaining several factors like Accuracy, Completeness, Reliability, Timeliness and Consistency.
By following a robust, strategic led process, you can link explicitly your strategy to your data, clarify what data you own and understand how valuable it is and to whom. You can create realistic aspirations for monetising your data, and you can prove your right to succeed by testing and overcoming areas of potential failure.
How we help Business become data-driven.
Becoming “data-driven” has been a commonly professed objective for many firms over the past decade or so. Consistent with these goals, companies have attempted to treat data as an important asset, evolve their cultures in a more data-oriented direction, and adjust their strategies to emphasise data and analytics. Although specifics vary, we work as data management consultants with expertise to identify above mentioned five or more stages in the data life cycle.
Strategic Advice
Regardless of your state within the lifecycle, our strategy can work with you to assist you perceive however best to spot, store and employ the information necessary to understand your vision for the longer term.
Strategic thinking, assessment, and coming up with are available in all sizes. From health-check to multi-year set up, we’ve consultants which will guide you thru with simply the correct level of involvement. a technique should work for you, therefore we tend to choose from evidenced frameworks, to deliver meaningful , concise, outcome-based ways that you just own.
The realisation of the benefits in the Data Strategy is dependent on the effective alignment of data governance and management with technology and IT infrastructure development, coupled with a clear articulation of business needs.
Data Exploration
Data exploration is the first step in data analysis and typically involves summarizing the main characteristics of a data set, including its size, accuracy, initial patterns in the data and other attributes. It is commonly conducted by data analysts using visual analytics tools, but it can also be done in more advanced statistical software, such as R, SAS, SQL, Python etc.
Before it can conduct analysis on data collected by multiple data sources and stored in data warehouses, an organisation must know how many cases are in a data set, what variables are included, how many missing values there are and what general hypotheses the data is likely to support. An initial exploration of the data set can help answer these questions by familiarising analysts with the data with which they are working.
Once data exploration has uncovered the relationships between the different variables, organisations can continue the data mining process by creating and deploying data models to take action on the insights gained.
Data Delivery Solution
To ensure that the project plan is being actualised, all aspects of the project must be monitored and adjusted as needed. To do this, follow these processes:
- Reporting: Have a metric to measure project progress and an instrument to deliver this information.
- Scope: Monitor scope and control changes.
- Quality: Measure the quality of deliverable and make sure that the planned quality is being met. If not, evaluate how to improve the quality.
- Schedule: Keep track of delays or blocks that impact the timeline of the project and adjust to stay on track.
- Cost: Monitor expenses and control cost changes.
- Final Look-back: Make sure the project deliverable have been completed as planned.
Typical engagements derived from one of our case study
- Assessments which help you perceive risks and opportunities in however you manage and use information.
- Technology road maps, assessments and evaluations that change your technology landscape, and make sure you get the most effective worth from your investments in technology- Proof of Concepts.
- Create the proper mix of individuals, process, and technology with strategic coming up with and style.
- Identifying and capitalising on internal and external information accessible to you.
- Creating a shared understanding of the corporate vision and the way information and analytics supports or leads it.
- Explore information science and analytics capabilities to work out people who will add business worth for you.
What we collaborate our Approach on
- Data Warehousing (DW)
- On-Line Analytical Processing (OLAP)
- Data Visualisation (VIS)
- Decision Analysis (What-if)
- Customer Relationship Management (CRM)
- Data Mining (DM)
- Machine Learning + AI (ML-AI)
