The prolonged effects of the pandemic, economic uncertainty and hybrid ways of working means the pressure is on organisations to be more agile, transformative and flexible than ever in order to adapt to rapid change. In this article, we take a look at why investing in BI and data analytics is critical to building business resiliency now and beyond the pandemic.
The pandemic is accelerating digital transformation and the need for data-driven decision making and business intelligence tools across so many industry verticals, from healthcare, HR, logistics to finserv and retail. In fact, Recent reports show the United Kingdom business intelligence market is projected to reach a CAGR of 9.2 % during the forecast period 2021-2026.
But according to data from the OECD’s skills database, the United Kingdom faces significant shortages in advanced analytics and technology skills. The centerpiece of the government’s support for the sector is a GBP 1 billion package to support the development of analytics in business, which is anticipated to boost the studied market.
Easy to use data analytics tools that ‘close the data skills gap’
For too long, business leaders have assumed that upskilling their workforce with data classes/certifications and investing in self-service tools would lead to a data-driven organisation. However self-service BI does not “close the skills gap”. Not everyone has the time or interest in becoming a data analyst or even data literate. Especially in today’s post-COVID landscape where teams are understaffed and people value their time differently in and outside of work.
In 2022, organisations will redefine what it means to build a “culture of analytics”. They will change the paradigm by bringing insights to workers in a more digestible way – turning to methods and solutions like embedded analytics that won’t require them to learn new skills or invest additional time. This is particularly important given new hybrid ways of working, where employees need easy access to real-time data, wherever and whenever they need it to be ‘data smart.’
Data platforms that combat ‘tool fatigue’
The rise of work-from-home and the digital acceleration brought on by the pandemic has also meant the rise in remote digital ‘collaborative tools and applications’, like Zoom, Slack, Teams, Google Chat, just to name a few. The downside to these tools is that they create distractions and inefficiencies, with workers jumping around from software to software or being forced to use tools that don’t fit into their personal workflow. As a result, we’re now seeing a new generation of the workforce experiencing ‘tool fatigue’.
It follows that investing in data/analytics solutions that add ‘yet another tool to the mix’ is no longer best practice. In fact, asking users to turn to yet another app for information is one way to guarantee that they ignore it.
Instead, invest in technological solutions that make it easy to ‘infuse analytics’ everywhere in the company.
We’ll start to see more organisations in 2022 delivering insights to employees directly ‘infused’ within their workflows via embedded analytics (for example, directly within Slack, Teams, etc.). In this environment, workers can make data-driven decisions without thinking twice and without any disruptions.
Critical mistakes to avoid when investing in BI/data analytics solutions
Over the past two years, data analytics has played a critical role in the way we’ve adapted and responded to the COVID-19 pandemic. In the UK, the government and public services opened up certain datasets to the private sector for the first time. We saw individual public services pool their datasets in other cases, allowing for more sophisticated data analysis.
For corporations, the proliferation of data analytics, technology, BI and AI means we’re entering an era where entire businesses can unlock decision making and generate unheard-of value across all industries and at all levels. This potential of unlocking the value of data analytics will remain untapped, however, if we don’t correct common mistakes we make when using data today.
Ignoring the power of ‘invisible analytics’: One of the biggest mistakes we can make is to look for data only after sourcing all our ideas and then ‘blindly follow what the data tells us’. Rather, analytics should work seamlessly alongside our natural creativity and expertise, making it ‘invisible’ where one begins and the other ends.
Over-reliance on traditional, standalone dashboards: As mentioned earlier, this requires us to deviate from our existing workflows. By ‘infusing analytics’ we can receive insights from data, front and centre in the apps we’re using, then easily leverage that data quickly and more accurately where and when we normally make our key business decisions.
Using spreadsheets and visually unclear data: One positive outcome of the self-service generation of BI has been the push towards data visualisation. We need to be deriving insights from data that are easily ‘consumable,’ ‘actionable’, and ‘understandable,’ not manually labouring over time-consuming spreadsheets.
‘Shiny new data toys’: Avoid choosing flashy visuals for novelty’s sake. Sometimes, bar and time-series charts are exactly what we need. If available, work with your company’s in-house experts and analysts to design the right visualisations in the right workflows to ensure that analytics continues to drive meaningful business decisions.
Who is getting it right?
One example of a thriving business investing in data analytics to accelerate digital transformation and business growth is Huws Gray, an independent builder merchant with over 100 stores across the UK. They recently leveraged Sisense, the leading AI-driven analytics cloud platform, to inject analytics across their organisation to support rapid expansion plans for 2022.
Before leveraging Sisense, Huws Gray was managing large volumes of data and could only analyse it manually via spreadsheets. This was time-consuming and unscalable. The overload of data also created inconsistency in reporting, with staff running reports from the point-of-sale systems and finding the results would vary constantly.
In 2020, Huws Gray turned to the AI-driven platform offered by Sisense, which has enabled Huws Gray to visualise the data they have with clear dashboards that are easy to understand. Sisense’s platform has also unlocked deeper financial insights for the company, by keeping accurate track of inflation and the cost of products.
Since implementing Sisense, Huws Gray enjoyed:
Time savings of up to 90%
Confidence in the accuracy of the data increase in basic terms by about 75%
Consistency increase by 100%.
Risk of data leakage and security tighten by over 75%.
Trend identification 50% more quickly.
“The Sisense dashboards also give Huws Gray employees a quick visual guide, and speeds up the process for staff to access the information they need, Mike Owen Jnr, IT Director at Huws Gray said. As we continue our expansion strategy in 2022, we’re confident Sisense’s robust, scalable platform can support us as we continue to scale.”
Building resiliency with ‘decision intelligence’
According to Gartner Top Strategic Technology Trends for 2022: in the UK, we’ll see a new era of ‘decision intelligence,’ which is a proactive, practical approach to improve organisational decision-making.
The key to effective ‘decision intelligence’ is that it models each decision as a set of processes, using intelligence and analytics to inform, learn from, and refine decisions. Decision intelligence can support and enhance human decision making and, potentially, automate it through the use of augmented analytics, simulations, and AI, Gartner notes.
Driving greater ‘decision intelligence’ is the evolution of analytics beyond descriptive analytics (what happened) and predictive analytics (what will happen) to prescriptive guidance (what to do about it).
By investing in the right data analytics tools that open up ‘prescriptive guidance’, customer service reps could be notified to reach out to potentially angry customers before they even call in. Sales leaders would react immediately to dips in revenue pipeline coverage due to upstream activities, without waiting until the end of the quarter. Retail managers could optimise inventory before items sell out by combining more than just sales data.
All this will mean prescriptive analytics will finally evolve from telling us just where the numbers are going, to helping us make smarter, proactive business decisions, paving the way for an exciting era of decision intelligence.
(Source: AI News Investing in analytics? Here’s what you need to know (artificialintelligence-news.com)
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