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    5 Characteristics that Insight-Powered Enterprises Share

    Many companies have had analytics, as a business discipline, on their corporate agendas for a number of years. Today, with the increased growth in data sources and the ubiquitous nature of analytics, a new environment is surfacing where robust analytics capabilities are needed just to keep pace. To obtain the most value from data in this evolving competitive environment, many companies are progressing beyond just using analytics, and are transforming themselves to build an insight-powered enterprise.

    To help gain a greater understanding of these transformations, Accenture Analytics studied what the most analytically mature companies are doing to gain tangible value from analytics, and how their practices might be adopted by companies not as far along the maturity curve.

    From the research, Accenture Analytics has identified five key characteristics that companies with mature analytics organizations generally share to pursue insight-driven transformation. Accenture has also outlined how companies could more effectively pursue this transformation and move from data to insights to outcomes for a competitive advantage.

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    5 Characteristics of an Insight-Driven Enterprise

    Click through for five key characteristics that companies with mature analytics operations generally share and how companies can kick-start an effective journey to become an insight-driven enterprise, as identified by Accenture Analytics.

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    Establish a ‘Center of Gravity’ for Analytics

    Establishing a center of gravity for analytics – often in the form of a Center of Excellence – not only builds talent and capabilities, it also supports consistency and high standards, while allowing functional resources to focus on key business problems and applying insights. Leadership is also a key element as organizations are elevating analytics by creating a chief data and analytics officer (CDAO) role. This role is responsible for both the vision and the implementation of the enterprise analytics activities, which includes developing goals and strategic plans to support the information, reporting the analytical needs of the company, while also acting as an agent to change the analytics culture.

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    Employ Agile Governance

    It’s crucial to build horizontal governance structures that are driven by one function or business unit, focused on outcomes and speed-to-value, rather than creating hierarchies that are slow to adapt. To that end, many companies are employing “fail fast” techniques, rapidly rolling out new capabilities and testing them repeatedly. If an idea is not working, it is quickly dropped so that the company does not continue to invest in things that do not add value. Perhaps the most critical part of agile governance is developing executive scorecards to measure performance. CDAOs are designing these to track performance against the key metrics, which might include: speed to standing up priority capabilities, pace of adopting new capabilities, and value realization. Actions and decisions are then made based on performance against key metrics.

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    Create an Analytics Pod Team

    High-performing companies with advanced analytics capabilities are fielding analytics teams with diverse skills. They are establishing “pod” structures or teams, which include a data scientist, analytics modeler, visualization expert, data engineer, business analyst and business domain expert.

    Organizations are using multiple mechanisms to source this talent, including internal development and hiring (on campus and from other companies), partnering with academia, and even crowdsourcing. Companies are also focusing on talent retention by developing reward and incentive programs to keep this high-caliber talent engaged.

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    Deploy Analytic Capabilities Faster

    New tools, technologies, and hybrid analytics architectures can help a company deploy analytics capabilities and achieve tangible outcomes at a quicker rate. Fast-moving companies establish a mindset, process and accountability that support pilot projects — quickly testing, learning, refining and implementing — with the intent to scale. Scaling analytics capabilities allows organizations to optimize analytics investments, rationalize vendors, suppliers, data and tools, and improve talent acquisitions.

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    Raise the Company’s Analytics IQ

    A company’s commitment to raising the analytics IQ for all roles within the enterprise is a distinguishing characteristic of a leading analytics organization. For example, an organization may implement an Analytics Academy that provides analytics training for functional business resources, in addition to the core management training programs. Additionally, leading analytics organizations have a vision of an “intelligent enterprise” – they are training resources to use new tools and techniques to improve decision-making throughout the enterprise and are also implementing innovative technologies, such as advanced data visualization, to communicate the value of analytics across the enterprise.

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    Put Analytics into Action

    Put analytics into action: Three Immediate actions to move forward on your journey.

    High-performing companies are accelerating their investments to create a more effective analytics operating model. To achieve this, there are three immediate priorities companies can take to kick-start their journey: (1) Select an agile approach – determine where your company is in its “analytics journey” and what it needs to do to develop sustainable capabilities to reach its goals; (2) Develop industrialized execution capabilities – experiment with team structures and other approaches to ensure that talent from all required disciplines is identified, organized effectively, and retained; and (3) Sustain the change – make a candid assessment of the company’s analytics IQ and undertake programs to raise the business acumen for analytics resources and the analytics acumen of business resources.

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