Successful analytics requires strong data management. It also requires speed to delivery to ensure timely and valuable insights. However, developing analytics that take both into account can be easier said than done. Surveying my many years of experience as an industry analyst and consultant, I can safely say that many organizations unfortunately take a piecemeal approach to their analytics investments.
The promise of interactive dashboards and visualizations drive initial interest in analytics beyond reporting and spreadsheets with their user-friendly features. But the complexities involved in consolidating multiple, disparate, and diverse data sources is frequently overlooked until after the solution is in place, creating a gap between time to implementation and time to value.
Longer time to value can be avoided by gathering data management requirements during any analytics evaluation phase, irrespective of whether an organization is new to business intelligence or expanding its current environment. By implementing data management as part of an analytics initiative, organizations can ensure:
• Better collaboration between IT and business units as requirements from both are needed
• The creation of a data management framework that takes continuous quality and integration into account
• Trusted information inputs and valuable business outputs
• Effective analytics and quicker time to value
• Tighter value chain between data acquisition and information delivery
But how is this achieved and what does strong data management mean for organizations?
Supporting continuous change and automation
Selecting a data management solution requires several considerations. But the most important ones, beyond features and solution capabilities, are level of automation and the ability to meet changing data requirements. Automation quickens processes and supports shorter time to delivery. Adding requirement changes as analytics environments become more complex means that automated data management can deliver the inputs needed to gain analytical insight faster. Ensuring overall and continuous data quality using an automated approach helps organizations build trust and deliver accurate insights to business users.
Both of these considerations should be incorporated into what an organization hopes to achieve on the business side as well. In too many cases, businesses focus their efforts on developing the right metrics but do not consider how data requirements will affect the outcomes of those defined metrics. Developing strong data management with a focus on delivering continuous, automated data integrity provides a framework for organizations that allows them to take advantage of faster speed to insight.
Enhancing speed to insight
Speed to insight depends on the ability to access valid and reliable data in a timely manner. Delivering quick time to value means taking the right data management processes, ensuring their automation, and creating analytics environments that are relevant and aligned to broader organizational goals. Organizations can no longer afford to reevaluate data quality after putting analytics tools into the hands of decision makers. This creates a lack of trust in the system and fails to provide the value promised by business intelligence insight. Consequently, as organizations begin to understand the intrinsic value of data, they will develop alignment between how information assets are managed internally with how they are delivered to create analytical insights and develop action-oriented outcomes.
21 en 22 maart 2023 Organisaties hebben behoefte aan data science, selfservice BI, embedded BI, edge analytics en klantgedreven BI. Vaak is het dan ook tijd voor een nieuwe, toekomstbestendige data-architectuur. Dit tweedaagse seminar geeft antwoord ...
4 april 2023 (Face-to-face én Live Video Stream) Schrijf in voor al weer de tiende editie van ons jaarlijkse congres met wederom een ijzersterke sprekers line-up. Op deze editie behandelen wij belangrijke thema’s als Datamesh, Analytics ...
5 april 2023 Praktisch en interactief seminar met Nigel Turner Data-gedreven worden lukt niet door alleen nieuwe technologie en tools aan te schaffen. Het vereist een transformatie van bestaande business modellen, met cultuurverandering, een herontwe...
5 april 2023 (halve dag)Praktische workshop met Alec Sharp This workshop introduces concept modelling from a non-technical perspective, provides tips and guidelines for the analyst, and explores entity-relationship modelling at conceptual and logical...
5 april 2023 (halve dag)Praktische workshop door Thomas Frisendal In deze workshop van een halve dag zal de Deense expert Thomas Frisendal laten zien wat graph technologieën in de praktijk betekenen. Hij zal ook laten zien hoe graph oplossi...
13 april 2023 Praktische workshop Datavisualisatie en Human Data Stories. Hoe gaat u van data naar inzicht? En hoe gaat u om met grote hoeveelheden data, de noodzaak van storytelling, data science en de data artist? Lex Pierik behandelt de stromingen...
8 t/m 10 mei 2023 Praktische workshop Data Management Fundamentals door Chris Bradley - CDMP-examinatie optioneel De DAMA DMBoK2 beschrijft 11 disciplines van Data Management, waarbij Data Governance centraal staat. De Certified Data Managemen...
11 en 12 mei 2023 Praktische workshop Data Governance & Stewardship door Chris Bradley - CDMP-examinatie optioneel Wat betekent Data Governance eigenlijk, hoe kunnen we het praktisch laten werken en wat zijn de implicaties? Deze 2-daagse cursus bie...
Deel dit bericht