Analytics is everything. Capital use is analytics; performance is analytics; brand-building is analytics; as is politics, social progress, even agricultural yield. Yet, despite its ubiquitous nature - providing foundations for better, more informed decision-making,  leveraging data analytics is far from simple. Particularly for today's CEO.

Reaffirming data analytics as vital expertise, capable of unlocking hidden growth, isn't new. However, profiling case examples of anyone other than a global game-changer is. So, while authority appears ubiquitous, reality paints a very different picture.

When seeking to maximise the benefits and opportunities analytics can foster, very few businesses succeed. Instead, intent on catching up hurriedly, many organisations misinterpret data and position themselves incorrectly - switching focus, utilising resources, and measuring perception in the wrong places.

Today, most organisational leaders appreciate the enormous potential behind analytics but, typically beginning with data identification and thorough collection, integration and transformation is where most inevitably struggle.

Putting analytics to work is a systematic process that requires prudent governance and careful management. But, as with any form of change, real challenges lie in wait between the tangible and intangible.

It is not only about new technology and killer algorithms but also seismic cultural change - where the ability to combine an analytics-first mindset with emotional intelligence and human understanding is proving to yield higher returns.

Only when analytics is empowered to flow throughout an entire organisation, can it genuinely deliver results - permeating every step of every process within every division.

So, with this in mind, here are a few steps to ensure analytics success:

Step one: Remove ambiguity

Imperfect information inevitably leads to biased conclusions, which are a significant risk. Not biased, in the sense of skewed results, but rather accurate data and results misalign with reality.

One of the most significant factors working against successful decision-making is a lack of reliable available data.

Without background information to confidently guide and inform, the decision sits with approximation and intuition. So while digitisation has helped resolve much of this problem, too many organisations still rely on intuitive assumption when conclusive data isn't available.

As in many emerging markets, incorrect or unrepresentative data often leads to inaccurate conclusions, as well as lengthy and costly mistakes. When objective information is scarce, one solution is to utilise learning algorithms to generate a solid fact-base. Supported by concurrent and active data gathering, accuracy is markedly improved and the ability to identify relevant solutions widens.

Also, imperfect information inevitably leads to biased conclusions, which are a significant risk. Not biased, in the sense of skewed results, but rather accurate data and results misalign with reality. To avoid this, the application of rigorous reality checks and cross-checks of output, from every possible perspective, becomes imperative. Only when coupled with real-life immersion, can data utilisation be as accurate as it can be.

Step two: Lessen the noise

When faced with the luxury of an overdose of data, adopting a hypothesis-driven approach enables organisations to distil data down to just the essentials.

Contrary to popular belief, there is such a thing as too much data, and the risk is twofold.

Firstly, it is difficult to avoid going overboard with lengthy, resource-intensive projects, without objectively advancing sufficient decision-making capability. So, when faced with the luxury of an overdose of data, adopting a hypothesis-driven approach enables organisations to distil data down to just the essentials.

As consultants, we have often found ourselves in situations where a problem is not immediately evident or that there may not be a problem at all. In such a case, identifying patterns and purpose drivers behind a business is all the more critical. Once drivers are apparent, a more accurate hypothesis can be constructed, ensuring that not historical intuition, but stakeholder influence on the organisation can be used to guide strategic alignment and future decision-making.

Step three: Take a stand

Any initiative undertaken isn't for maximising short-term results, but long-term returns.

General performance and market data is a relatively simple mathematical exercise. However, when the need for personal information, behavioural patterns and personalised transactional data is required, expectations grow.

Every day, stakeholders entrust organisations with the management of personal information. However, when trust is questioned, as has been the case with mining and manipulation of mainstream social media data, subsequent damage is tough to contain, and implications are widespread.

One way to avoid controversy is to adopt a purpose-led, values-driven approach to data analytics. Do this by first determining what kind of information is needed. Secondly, understand why specific data should, or shouldn't, be used. Thirdly, appreciate how it will be used to improve lives and stakeholder experiences.

Any initiative undertaken is not for short-term results, but long-term returns. So, also create a transparent social contract with all stakeholders - highlighting the benefits of sharing personal information and your organisation's commitment to protecting privacy.

Step four: Go, long

Future success is rooted in objectively measurable information and based on thorough analytics, with business continuity and sustainability being both measurable and adjustable.

It is clear, in an increasingly complex world, certainty is a rare and valuable commodity. While some may consider it a utopian ideal, what any organisation does should always be delivered in the service of continuity and perpetuity - both for itself and the world at large.

Future success is rooted in objectively measurable information and based on thorough analytics, with business continuity and sustainability being both measurable and adjustable. Where, for analytics to work, a stable, reliable and sustainable foundation must form - a strong bedrock that empowers positive, profitable, long-term relationships.

Step five: Consider the human side

As they move towards making analytics more purposeful and accountable, organisations require a deeper understanding of humans as well as machines and data.

Being data-driven isn't about handing over control of decision-making to a machine. It is about taking the output, gleaning insight, and figuring out how to apply it across all functions and performance improvement.

As they move towards making analytics more purposeful and accountable, organisations require a deeper understanding of humans as well as machines and data.

Without an appreciation of these constituents, clarity surrounding purpose, strategy and execution are unclear. As such, the human side of analytics provides data scientists with the context, meaning and understanding needed to communicate the output of statistical models to executive decision-makers better.

Translated through compelling narratives and visualisable, conceptual opportunities, organisations can explore new and different ways to identify new ideas and innovations. Where, for leaders, understanding the process can be relatively straightforward:

1. Adopt an iterative approach to developing creative hypotheses. Test and validate ideas with data and explore use-cases;

2. Be empathetic in your attitude towards analytics-enabled transformation. Develop an understanding of stakeholder needs, both from ethnographic and hard-data perspectives. Try to utilise qualitative as well as quantitative inputs;

3. Tell stories with data by co-creating compelling narratives. Provide actionable advice and drive the adoption of insights within the organisations. Bottom-up as well as top-down; and

4. Drive and nurture a culture of continuous improvement. Recur the cycle of data-driven performance uplift across every facet of your business. Combine the best of advanced analytics with creativity and design.

This post skims the surface of analytics, but its purpose is to show that future analytical success is dependent on both objective and subjective influences and drivers - with the latter being vital in helping leaders make the best-informed decisions - enabling organisations to achieve the highest levels of meaningfulness and value.