Data strategies: the fundamentals
As Business Intelligence tools, Data Analytics processes, and data storage capabilities continue to become more and more advanced, many businesses are realising the continued importance of evaluating their use of data to gain maximum value.
To leverage collected data to reach insights that inform trends, campaigns, challenges and more, businesses of all sizes are implementing defined data strategies. These outline goals, objectives, and processes in which data may be collected and used – ensuring data is utilised to help companies reach their full potential.
However, as the architecture of each business is unique and holds its own set of challenges and factors, the creation of reliable and high-quality data strategies differs greatly. The strategy of an internationally established organisation may be unrecognisable from that of local SMEs, impacted by a wide range of factors.
Some factors that must be considered and navigated to create a strategy that is effective and optimised on an individual level are:
- Current data processes and size of architecture
- Technical capabilities of internal teams
- The role and presence of ongoing quality assurance
Read on to learn more about these factors, and how they impact the efficiency of data strategies.
Factor 1: The size of the business
Forming a successful data strategy is a multi-faceted, multi-layered approach with many different entry points.
If you’re an established business with a traditional data warehouse that facilitates standard reports and provides information about sales and finances, a data strategy may focus more on the progressive next stage. Businesses in this position may begin to focus on more advanced requirements such as forecasting capabilities and cognitive services.
However, for smaller, less-established businesses that are beginning to form their data analytics process, an optimised data strategy may focus on establishing the core fundamentals. This may include ensuring that well-structured core data can facilitate some primary functions to inform insights unavailable through contemporary software.
These processes may allow smaller businesses to make current operations smoother while reducing manual processes with traditional approaches. For this purpose, businesses may consider implementing a chosen visualisation reporting tool such as Power BI.
As a business develops, so too might its ongoing data strategy. From beginning to realise new potential avenues to leverage data for advantage, or implementing scalable solutions as datasets grow – such as migrating to cloud storage – data strategy can continuously be monitored and evaluated to ensure optimised results.
Factor 2: Technical capabilities of internal teams
The technical capability of your internal teams will also affect the direction your data strategy can go in, companies may have particular skills that lend themselves to choosing one tool over another, or may have limitations based on the infrastructure partners or a chosen cloud platform. They may have investments in existing software and infrastructure they want to try and leverage.
Good Data Strategy needs to consider these items, and not just be an evangelist for the latest and greatest. Furthermore, they will also be able to identify streamlined pathways to develop enhanced data strategies that deliver the most value possible.
As technology continues to evolve and develop, those without technical specialities will still be able to leverage useful insights from their data. With market-leading platforms such as Power BI gaining evermore popularity, business is empowered with tools that emphasise accessibility and ease of use.
This focus on ease of use has permeated in other sectors. With the development of Cloud-based data storage and reporting, as well as the advent of SaaS, accessible solutions that would previously have seemed impossible are now well within reach.
Advanced education as a tool
While technical capabilities can affect how a data strategy is built and maintained, the emergence of new fields in the academic space allows students to develop advanced analytical and predictive modelling capabilities. These niche skillsets allow smaller businesses access to resources previously unavailable. While beforehand predictive analytics were only available to those with immense budgets and expertise, new SaaS offerings can make this capability more commonplace.
For those businesses unable to gain access to these advanced analytics, the team at DataShapa can provide ongoing services and support to enable businesses to reach data-driven insights previously impossible to find.
Factor 3: The role and presence of ongoing quality assurance
Once a data strategy is implemented, businesses must strive to maintain consistency and regulate data use. This can aid in ensuring that all members of a business are aware of the role of data, the processes that must be followed when interacting with data, and the importance of data that is of high quality and is consistent.
Without ongoing data quality assurance, businesses may suffer from low-quality insights. Missing records may influence decisions, introduce unwanted bias, and potentially cause misunderstandings with dire consequences – such as mishandling budgets and targeting incorrect audiences for marketing campaigns and more.
Low-quality data may also lead to other potential consequences that disrupt complete data strategies and more. With no clear consistency, analysts may lose their trust in automated processes and cloud storage capabilities – reducing the potential value and return of investment of advanced strategies.
Get in touch to learn more
To find out more information about the importance of creating a valuable data strategy, as well as the many advantages of Business Intelligence toolsets, read more of our insights here.
Alternatively, for any queries or business enquiries, contact us here. We are always happy to help and aim to respond to all questions as soon as possible.