How a Data-Driven Culture is Essential to Optimising Data Analytics

16 April 2021

How a Data-Driven Culture is Essential to Optimising Data Analytics

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As datasets become more complex and accessible than ever before, continuous challenges at all stages of a data strategy threaten to destabilise and diminish the quality of insights. In an age of increased automation and Cloud-hosted data, we must examine the role of people and culture within the optimisation of data analysis to maximum effect.

From negotiating low-quality data that may decrease trust in decisions, to evaluating the need for establishing data literacy that spans the entire business, people continue to play a vital role in maintaining and delivering key insights through analysis.

Below, we examine the current challenges that the data analytics landscape continues to present, as well as how many aspects of a data-driven culture is vital to resolving issues and restoring quality.

Data Analytics: The current threats

As the capabilities of data analytics continue to evolve and adapt to new structures, and vaster sets, of data, harmful issues continue to persevere. Three consequences of a poor relationship between people and data are:

1. The construction of data silos

Data silos continue to harm many vital areas within an organisation, from upper management to finances and marketing. A data silo occurs when certain data is reworked or collected while being closed off from the rest of the data present. This creates a package or ‘silo’ that remains separate from other data sources. This gives many key decision-makers, as well as other teams within a business, an incomplete view of current data.

2. An overarching lack of confidence in collated data

A lack of data literacy influences the quality of data analysis on a large scale. Conclusions derived from poor data literacy is liable to change and shift, skewing conclusions and results. This possibility ensures that no results gained can be completely trusted, leading to a culture that pushes data to the sidelines, instead of using it as a central pillar to support operations. For successful growth and development, this must change.

3. Incorrect framing of data analysis procedures within overall operations

Data analysis remains pushed to the side of many operations within a business, being viewed as an addition to current IT processes, rather than its correct position as a core function that surrounds the entire operation of a business. This limits efficacy, reduces data literacy, and disrupts the possibility of establishing a data-centric culture that empowers, rather than distracts.

Culture as a solution

Through the introduction and optimisation of a data-driven culture, the possibility of these issues continuing to affect businesses is lessened. Instead, businesses may expect high-quality data that allows successful insights that drive opportunities, engagement, and growth, with confidence and trust throughout.

Reframe your data strategy to eliminate poor insights

A primary strategy that serves to reduce the possibility of poor data quality and the construction of harmful silos lies in improving the overall prioritisation of data throughout a business. A core principle in achieving this lies in reframing the function of data, and how it affects numerous functions.

Rather than gaining insights based on current collated data, businesses should move towards restructuring how data is stored and analysed to gain the insights and results desired. In doing so, a data strategy’s process is not only examined throughout the entire operation, but it is placed as a core pillar that moves to inform with visibility and clarity.

Improve data literacy through experience and familiarisation

Company-spanning data literacy is crucial to not only improving the overall quality of your data but also eradicating any harmful data silos that may have been constructed. Much like general literacy, data literacy focuses on developing skill, comprehension, and understanding for those working alongside data.

Data literacy must be practised and considered across the entire business, not simply analysts or key decision-makers. To eliminate the construction of data silos, and instil confidence and trust in sourced data, all teams must be aware of the overall processes and principles surrounding the current data strategy.

There are several effective ways to increase company-wide data literacy, from paid training courses to webinars and external reading. However, one vital technique remains efficient and invaluable: immersion.

One of the best ways to inform and develop data literacy is through encouraging transparency and communication on current data processes and more. Allowing your team to personally view the overarching framework, while simultaneously supporting any queries they may have, provides an opportunity to establish valuable consistency and open clear channels of communication – both crucial factors in eliminating data silos.

Establish a consistent framework approach to boost data quality and trust

Once a data strategy has been optimised to reach desired insights, and data literacy has been increased throughout the business, a vital factor to cement the overall quality of data collated and analysed lies in establishing a consistent framework approach.

As with data literacy, an inconsistent approach to data analytics can produce varying results that damage trust and confidence in collated intelligence that should be used to empower, rather than distract.

Through the construction of a consistent approach, businesses can expect to reintroduce structure, visibility, and confidence in data – vital for the optimisation of analytics and the retrieval of valuable insights.

The key to the creation of a consistent approach to data analysis lies in communication throughout the business. As more and more of your team is made aware of the structure and process in which data should be stored, examined, and analysed, the possibility of silos and varying results drawn from incomplete datasets is lessened, ensuring critical decisions are executed with confidence.

The future of data analytics: The Cloud and automation

Data analytics continue to develop and advance, with new technology and processes ensuring the need to re-examine analysis processes regularly. Among recent developments, both the shift to Cloud based services and the increasing use of automation and Machine Learning has opened discussion on how people continue to interact with data processes.

The recent pandemic has created the need to re-examine common contemporary data storage and processes, due to the significant demand for virtual desktops and work-from-home capabilities.

As cloud-hosted data continues to trend – providing a cost-effective, efficient, and simplified alternative to on-premises hosting, data literacy must evolve to consider data processes within the Cloud. An effective data-driven culture will consider the evolving needs and demands of the Cloud to ensure a seamless transition.

Automated modelling through ML capabilities has also presented challenges to the ongoing relationship between people and data.

However, while capabilities to automate workflows and create predictive models are developing, Human insight, experience, and forethought – concepts that can’t be substituted – are still required.

Informing data strategies with confidence the DataShapa way

Here at DataShapa, we’re committed to empowering businesses to gain critical insights to inform market-leading decisions through high-quality data analysis. With our unique methodology, we’ll work alongside you to examine the current challenges, needs, and requirements of your data strategy, before establishing an overarching process that adds measurable value to your system.

From consulting services to long-term support through retained engagement, our list of services and products are uniquely designed to restore confidence and trust in your data.

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