¿Cuáles son las mejores formas de garantizar que su estrategia de TI esté basada en datos?

Consider a retail business aiming to enhance its online customer experience. The clear vision is to increase satisfaction, boost sales, and improve operational efficiency. By collecting and analyzing data on customer preferences and behaviors, the company implements IT strategies such as personalized recommendations and optimized inventory management to align with its business goals, ensuring continuous improvement.

Data and Business Intelligence are the easiest targets for flights of fancy. Organisations want to hire data scientists, talk about advanced compilation of insights, even perform machine learning, before they even have their basic data standardised and centralised. Start by identifying your holy grail, and then work backwards *all the way*. You'll find much more value in your initial data strategy being extremely simple. Do you even have a single hierarchy for your geographical structure? If no, why do differences exist? Defining an objective in year 1 to agree a geography standard (and accepted differences) is a perfectly valid goal, and this type of master data will link to everything else on your maturity journey.

Less unstructured data and more SQL! If the company does not have that implemented provide a solution and restructure. Not existing is not an excuse, sorry!

Less unstructured data and more SQL! If the company does not have that implemented provide a solution and restructure. Not existing is not an excuse, sorry!

To anchor your IT strategy in data, start by setting clear data-centric goals. Invest in robust analytics tools for deep insights and ensure high data quality. Crucially, integrate data across departments for a unified view. Cultivating a data-driven culture is key, encouraging all teams to base decisions on solid data analysis. Regularly hold data review meetings for alignment and continuous improvement. Data security and regulatory compliance are non-negotiable, ensuring trust in your data's integrity. Lastly, maintain agility to adapt strategies based on evolving data insights, and consistently measure the impact of data on achieving business objectives.

Define key performance indicators aligned with strategic objectives. Implement robust data collection processes, utilizing tools and technologies. Regularly analyze the gathered data to derive actionable insights. Incorporate feedback loops to adapt strategies dynamically. By emphasizing the continual collection and analysis of pertinent data, your IT strategy remains responsive, informed, and aligned with organizational goals, enhancing decision-making within the dynamic landscape of IT consulting.

I strongly believe that data strategy needs to sit in the middle of the organisation. Often, people attempt to push data initiatives from the top downwards; it rarely works, because of differences in data structures, or lack of depth in global data sets compared to local data. Similarly, a single team or market cannot be expected to start pushing their high quality, customised data up to a collective region. They won't take note of differences between teams / geographies and don't have resources to scale-up. As such, successful data projects pull in the benefits of centralisation, cost-reduction and technology power from above, with the processes, high data quality, and customisation of the front-line teams.

Identify the critical data elements for organizational objectives. Assess existing IT capabilities and infrastructure. Bridge gaps through strategic investments or upgrades. Implement data governance to ensure quality and security. Foster a culture of data literacy. Continuously reassess alignment as needs evolve. This targeted alignment ensures that your IT strategy harnesses the power of data, optimizing decision-making and enhancing overall effectiveness in the realm of IT consulting.

Data strategy requires a robust but scalable data warehousing solution, technology-enabled data governance, and supporting platforms to collect and transform data. But one thing that isn't considered up-front is how integrated your data strategy has to be with everything else. A simple example would be the plain old externally-facing website; any company mature in data would be using their data warehouse to drive their website content, instead of flat html files of content that have to be updated whenever a product or geography changes. Almost everything in a company's operations uses data; make sure you plan from the start to drive all of these operations from your centralised data hub.

While individuals should own their data, there is also value in responsibly sharing it. As stakeholders, we understand our data best and can contextualise it. Participating in data sharing allows us to support win-win outcomes, like improved products and services. But it requires dialogue - companies should communicate how data is used and enable stakeholder participation in data governance. This fosters trust and benefits all, upholding the value that data should enrich lives. Overall, balanced data ownership and sharing can promote innovation that serves societal good.

Ah, the golden question - who owns the data strategy, who owns the data itself, who is responsible for defining data standards, and who drives data success through the organisation. Data is everyone's responsibility, and a culture has to be formed over time where everyone cares about the data within the company. At first, this culture will have to be more "stick" then "carrot"; the benefits of data governance, alignment, centralisation and integration won't be seen by the majority of staff. Once the basics are in place and you're ready to start leveraging your simple data structures in other processes and technologies, the "carrot" comes into play as staff realise their effort is ensuring higher data quality and less repetitive work.

A data strategy or program is certainly one that will not have an end-point; you should be reviewing a regular roadmap that has small but tangible quarterly milestones. It is an extremely suitable technology service line for agile methodologies and regular sprints.

I'm not sure we fully addressed the topic's core question: "What are the best ways to ensure that your IT strategy is data-driven?" It starts with the firmly held belief that: an objective fact-based approach to decisions will yield better results than intuition and experience alone. Do leaders believe that data analysis helps them make better decisions? If they don't believe this, trust the data, and have the discipline to act on the answer, then you "may" need to convince them with a retrospective analysis (demo) based on prior decision history/data assuming it's available. You may need to do some JIT education (e.g. statistics). Talk to them about decsions they're struggling with and data obstacles needed to support them.