4 pillars of analytics strategies

Leading analytics functions in fast-paced environments.

Data analytics dashboard on a laptop
Carlos Muza (CC0)

Why you need an analytics strategy

If data is the new oil, then analytics is how we refine it. Analytics enable the extraction of high-quality, timely and actionable insights to help leaders make better-informed decisions.

Most analytics functions nowadays are centralized divisions of decision science experts. They support internal teams, providing reporting, dashboards, custom analytics, business intelligence, and in some cases advanced data products that leverage deep industry knowledge.

But without a clear strategy, the rapid changes in the analytics world can lead the team in the wrong direction, with expensive test and long integrations that can cost the executive team valuable time.

The new multi-disciplinary analytics leader

Traditional reporting started with the Accounting and Finance teams and later moved to the digital space in the IT and Marketing departments.

Today, big data analytics is a multidisciplinary area of expertise that requires a deep understanding of finance, marketing, and technology.

The new analytics leader needs to be able to lead and inspire high-performing teams of decision scientists, be a visionary thought leader, and influence the organization with strategic high-quality predictive analytics.

As an expert, the head of analytics needs to be a key contributor to the global vision, and develop consultative relationships across the organization, to enable leaders to make data-driven decisions and stay ahead of competitors.

How to develop a good strategy

A good analytics strategy needs to enable the development of a data-driven culture that identifies signs of change and drives business transformation. To succeed, it is critical that the organization develops talent and drives innovation, to prepare for the future.

In fast-paced environments, it is critical to create systems that identify signs and trends that can tell us if we are the Frog in boiling water.

The parable of the boiling frog describes a Frog which, when put directly into boiling water jumps out, but when put in lukewarm water that warms up slowly, the perception of danger disappears and the Frog dies.

Today, we know that if the water is gradually heated and sufficiently hot, the Frog will still jump out of the pot, but for large organizations, there is simply not enough time to jump.

Of all the FTSE companies in 1999, only 49% were still operating by 2015, a harsh reminder of how hard is to transform and adapt.

The 4 pillars of success

Here are four key points to help develop a solid analytics strategy for any industry that can be adapted across cultures and company sizes.

1. Create a data-driven culture

  • Engage with internal clients to improve delivery of analytic projects needs
  • Audit the analytics tools and report catalog, clean duplication, and overload
  • Monitor Business vital signs creating Dashboards for a culture of transparency
  • Enable storytelling visual systems that infuse analytics across the organization
  • Develop insightful, consultative research led by strong methodology

2. Support business transformation

  • Provide thought leadership in analytic techniques and new solutions
  • Develop trust relationships with business leaders and understand their needs
  • Align with local KPIs to develop deep analysis and custom solutions
  • Develop skills and align old capabilities to new organization needs
  • Enable quality control systems and audit and compliance tools and training

3. Develop talent

  • Recruit, develop, and retain a diverse world class team
  • Encourage a culture of trust that rewards the right behaviors
  • Develop continuous learning tools that support internal and external clients
  • Identify data points opportunities across the digital and physical customer journey

4. Drive Innovation

  • Educate in the creative use of data and analytics to solve business problems
  • Identifying external trends to inform strategic decisions
  • Encourage a culture of data-driven innovation by bringing in external speakers
  • Create and test new ways to transform current practices

Case studies of successful projects

Here are some examples of implementation success stories.

Banking industry

hana overview SAP

DZ Bank reduced their risk and improved profitability by leveraging real time actionable insights. Delivered a single point of truth for financial and regulatory reporting and one source for analytical apps using SAP. The German enterprise software giant’s SAP S/4HANA simplifies finance, logistics, inventory management and supply chain management with faster implementations and rapid results.

Technology industry

qlik sense cloud Qlik

Cisco was looking for a self-service solution for actionable insights and identified Qlik as a partner.

“After exploring several BI platforms, it was clear QlikView best fit our needs,” said Barshay. “We were impressed by the user-friendly nature of the product and the intuitive ability to perform ‘wildcard’ searches against large amounts of data in real time.”

Qlik is the provider of QlikView and Qlik Sense, business intelligence & visualization software. Cisco identified more than $100 million in support renewals and $4 million in cost savings.

How to be a data champion:

Assess

Assess how your data fits the needs of the business today. Is the access to information fast enough? are there any concerns about the quality? Is the data telling the right story?

Call to action

Develop a change management culture with a call to action that shows the need for change. A good example is a CEO or a large financial services company, tired of the number of reports his leaders were receiving, decided to shut down all reports and turn on only those that at least two employees requested. 90% of the reports were never requested and the saving on resources and processing time was allocated to new innovative data solutions.

Review

Start with a blank slate and look at the long-term strategic objectives of the organization. Set organization-wide goals and measures on what is needed and how to achieve it with data, then and set KPIs for the team to focus on and continue to review and update them as needed.

Start today. If you are not actively leading your data analytics strategy, you are not fully leveraging the power of data in your company. Take control and be a champion of data-driven decision making in your organization.

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