Overcoming the hurdles of becoming a data-led organisation

Can you imagine your life without data analytics? You might be surprised at how often you leverage data analytics in your daily life.

Without this ubiquitous resource, you'd have to give up your daily traffic updates based on vehicle behaviour patterns, and settle for whatever is left at your local grocery stores because those in charge of inventory wouldn't know what products to stock based on demand. You'd also have to do without easily checking your bank statements online, and receiving sports analyses about your favourite players' historic sports achievements.  

Now, imagine an organisation without data analytics. Despite the enormous benefits of adopting analytics, several organisations are still sceptical. The recent digital readiness survey by ManageEngine found that Singapore has witnessed a 94% increase in use of business analytics in the last two years. It also states that IT departments are driving over half the adoptions for their organisations.

But the transition to analytics-driven processes is fraught with challenges, preventing many from realising the full value of their data.

Below are just some of the primary challenges faced by  adopting analytics, and offers guidelines for overcoming them.

1. Data silos

Not all of an organisation's data is stored in a central data warehouse. It's mostly siloed in dozens of different applications or databases. For instance, marketing data is stored in CRM applications, supply chain data is stored in local or cloud databases, and facilities and internal IT management data is stored in monitoring and management applications.

There are two ways to combat this challenge. One, use an integrated analytics platform that liaises with all applications and databases, and unifies them under one console for analysis. Or two, bring all of the organisation's data into a central warehouse. The second option is expensive and time-consuming, but it fast-tracks analytics adoption.

2. Budget constraints

Many organisations don't have large budgets to invest in analytics applications that cater to the entire organisation and provision access to all employees. This "boil the ocean" approach is bound to fail in a short run time.

An iterative approach, with need-based access to analytics, is best suited for organisations that are strapped for cash. Start a pilot program in one department of one branch of your organisation and then expand to encompass other departments, after realising success in the first department.

3. Demand for immediate results

Analytics adoption has long since been mythified as the Goliath that only guzzles money and delivers very little value. The secret to discovering value from adopting analytics is by prioritising the problems. When solved, this will deliver maximum impact.

For instance, Coca-Cola, a multinational corporation, used analytics to invest heavily in operational efficiency over marketing – as the latter is known to provide results slowly. It used analytics to evaluate crop availability and taste variance, and identify more than 600 flavours of oranges to develop a proprietary algorithm that ensures a consistent taste and texture for all orange-based drinks for its Minute Maid and Simply Orange brands. This helped the beverage company gain a loyal customer base, and create uniform and standard flavours for its drinks.

4. The IT-Business conflict

IT departments play a vital role in helping organisations embrace data analytics. They help set up the analytics application, facilitate user access, and create guidelines for the usage and sharing of data.

While this sounds great theoretically, in practice, IT departments exert complete control over data and often do not liberate data for business users to test their hypothesis and theories. Instead, the IT department should provide business users fine-grained access permissions (such as read-only, read-write, or interact) to access and analyse data.  

5. No clear-cut analytics strategy

Organisations adopting analytics can go on for years without realising any value from the move unless they have a transparent analytics strategy. A data analytics strategy should encompass organisation goals and objectives.

For example, an analytics strategy to improve website performance and engagement should dictate the webpages that are going to be assessed, the metrics that will be used to measure performance and engagement, and help the organisation develop a plan to break down newly-obtained insights into small, bite-sized action items that can be implemented to improve performance.

6. Lack of skilled analytics leaders and advocates

The misconception that data analysis requires specialised skills and intensive training has relegated data analytics to statisticians, analysts, and database experts. While this may work in some cases, data analysts often don't have the heart for business as much as a passion for statistics.

For example, a data analyst looking at a marketing campaign data can tell you that the latest campaign has failed and how, but often cannot advise what to do to achieve better campaign results.

Getting the desired insights for marketing campaigns and for other vital business projects is straight-forward and involves teamwork.

Share data for consumption by business users, gamify data analysis, and incentivise business users to develop solutions themselves using analytics. Start with departments that can easily realise the value of analysis, such as the sales teams. Empower them with data to achieve say, 15% more sales in a month. Then turn them into analytics advocates to actively encourage other departments such as marketing or dev-ops to use analytics.

7. Antiquated data culture

Data culture, or more precisely, decision-making culture is the biggest challenge to adopting analytics. In several organisations, decisions are based on gut instinct, and not facts and figures. In places where leaders lean towards analytics, the attempt is often half-hearted at best—an attempt to validate their own beliefs and biases, rather than analyse data objectively.

Amazon and Walmart, the biggest names in online retail merchandising, beat this challenge by democratising data to its partners and sellers. Instead of telling their partners what products work better on their channel, Amazon and Walmart made behaviour data available to large sellers, such as Unilever, P&G, Kimberly-Clark, and the Altria group. This helped these sellers and in turn, Amazon and Walmart, eliminate biases and gain actionable bits of information on the best-selling and quick-selling products.

8. Concerns over data security

Early in January 2021, the payments processor Juspay reported a massive security breach of customer details from retail giant Amazon and Swiggy, the Indian food delivery app. More than 100 million debit and credit card user information were leaked online.

While this proves no organisation is immune to data security risks, formulation and adherence to security and privacy frameworks, such as General Data Protection Regulation, can go a long way to minimise the number and impact of security breaches. 

Applying analytics to your business

Despite the proven benefits of analytics, organisations continue to encounter multiple setbacks while adopting analytics. From lack of skills to budget constraints, security issues to a demand for immediate results, the barriers to smooth analytics adoption are numerous.

However, it's not an impossible challenge. With intelligent adoption of best practices and formulating a clear strategy, it's possible for organisations to successfully  embrace analytics in all facets of their operations.

Sailakshmi Baskaran is the Product Consultant at ManageEngine