A culture of data literacy holds the key to democratising data across an organisation

In order to drive maximum business value from data analytics strategies, organisations must not rely on IT departments to determine the data strategy

SMBs have increasingly invested more time and money in their data and analytics initiatives over the past five years according to Gartner. However, many are falling short of unlocking the maximum benefits available to them, due to just procuring technologies and not enforcing an organisation-wide culture of data analytics.

Aligning data and corporate startegies

James Don-Carolis, Managing Director at TrueCue, is urging business leaders to not only review how data analytics is conducted and viewed across their organisations, but also to align their data strategy with their broader corporate strategy. By extracting maximum business value from their data analytics strategies, businesses will be in better position to navigate the ongoing disruption.

James explains: “While it is encouraging to see many SMBs are beginning to realise the copious amounts of benefits data and analytics can bring to their organisation, it is concerning to see many are falling short of achieving valuable business outcomes from their investments.

“With Gartner finding that 73% of mid-sized organisations are still labelled as having low data and analytics maturity, it is important – particularly in the current climate – that the benefits from investments are being maximised, not underutilised.

Remaining competitive post pandemic

“An organisation-wide culture of data and analytics is fundamental to any organisation striving to achieve greater data maturity. With virtually all organisations facing some degree of industry and business model disruption often created by digitalisation, maximising the value of data analytics investments is critical for organisations looking to remain competitive beyond COVID-19.

Related post:  How BuddyCRM software helped the IoD achieve double digit sales growth

“To put it simply, data and analytics maturity is a key competitive differentiator, and is arguably a necessity for every business to prioritise as we become more reliant on the digital economy. We know from our research that data-driven organisations are 23x more likely to acquire customers, 6x as likely to retain customers, and 19x as likely to be profitable as a result of building this capability,” James adds.

With this in mind, there are five stages businesses must navigate on their journey to achieving a high-level of data analytics maturity, however as a starting point, they must understand the maturity journey their organisation needs to embark on:

The first phase – manual preparation of reports driven by ad-doc requests

On average data workers are spending seven hours per week manually updating formulas, pivot tables, cell and sheet references, and smaller organisations do not usually have the resources to facilitate this. To address this issue going forward, as a first phase, employers should introduce more efficient tools, both in data visualisation and preparation, as well as training staff to utilise these correctly.

The second phase – generating insights

Successful organisations will train departmental analysts with agile data visualisation tools to build dashboards and provide a consistent capability, as well as introducing best-practice guidelines for usage and standardisation via templates.

The third phase – automation, robustness and security

With central data teams responsible for standardising approaches, while bringing data functions into a single location to help with inconsistent processes, automation, robustness and security are key to the third phase.

Related post:  Microsoft Future Decoded 2019: AI and People dominate the agenda

Incorporating an effective data management strategy at this stage helps ensure the automation of repetitive processes that greatly improve the efficiency and productivity of any organisation, relieves internal resources (both human and machine) and reduces manual labour and errors.

The fourth phase – enterprise maturity and cloud strategy

The enterprise data platform is designed to support the overall data and analytics strategy, provide a robust, governed, consistent and scalable capability, and service both standard and ad-hoc analytics as well as supporting advanced analytics such as machine learning.

Cloud strategy is also core to this stage, leveraging all the benefits and flexibility cloud resources offer. Automation at this stage can deliver many benefits such as time – and resource – savings, standardisation and flexibility.

The fifth phase – analytics-led corporate strategies

Organisations that utilise both internal and external data in a systematic way to drive decision making across the business are proven to outperform those that don’t. Creating a position of leadership that has the responsibility and ambition to prioritise data within their organisation’s culture is key to achieving data maturity at this utmost level.

Accessable and affordable cloud technologies

James concludes: “It goes without saying that the data maturity journey can be all the more challenging for SMBs who do not have the same funds or resources as their larger enterprise counterparts. So, it is understandable why they often struggle to derive maximum value from their data.

“However, with advancements in accessible and affordable cloud technologies, such as cloud data warehouse automation, improving data and analytics capabilities has become much more of a reality for organisations with smaller budgets and less technical resources.”

You might also like:

  • Azzure IT

  • Epicor

  • Exel

  • Medatech

  • Winman

  • NoBlue